Overview

Dataset statistics

Number of variables122
Number of observations10
Missing cells406
Missing cells (%)33.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory1.8 KiB

Variable types

Numeric14
Categorical105
Boolean3

Warnings

NAME_HOUSING_TYPE has constant value "House / apartment" Constant
FLAG_MOBIL has constant value "1" Constant
FLAG_CONT_MOBILE has constant value "1" Constant
FLAG_EMAIL has constant value "0" Constant
REG_REGION_NOT_LIVE_REGION has constant value "0" Constant
REG_REGION_NOT_WORK_REGION has constant value "0" Constant
LIVE_REGION_NOT_WORK_REGION has constant value "0" Constant
REG_CITY_NOT_LIVE_CITY has constant value "0" Constant
NONLIVINGAPARTMENTS_MODE has constant value "0.0" Constant
NONLIVINGAREA_MODE has constant value "0.0" Constant
FONDKAPREMONT_MODE has constant value "reg oper account" Constant
HOUSETYPE_MODE has constant value "block of flats" Constant
EMERGENCYSTATE_MODE has constant value "False" Constant
FLAG_DOCUMENT_2 has constant value "0" Constant
FLAG_DOCUMENT_4 has constant value "0" Constant
FLAG_DOCUMENT_5 has constant value "0" Constant
FLAG_DOCUMENT_6 has constant value "0" Constant
FLAG_DOCUMENT_7 has constant value "0" Constant
FLAG_DOCUMENT_9 has constant value "0" Constant
FLAG_DOCUMENT_10 has constant value "0" Constant
FLAG_DOCUMENT_11 has constant value "0" Constant
FLAG_DOCUMENT_12 has constant value "0" Constant
FLAG_DOCUMENT_13 has constant value "0" Constant
FLAG_DOCUMENT_15 has constant value "0" Constant
FLAG_DOCUMENT_16 has constant value "0" Constant
FLAG_DOCUMENT_17 has constant value "0" Constant
FLAG_DOCUMENT_18 has constant value "0" Constant
FLAG_DOCUMENT_19 has constant value "0" Constant
FLAG_DOCUMENT_20 has constant value "0" Constant
FLAG_DOCUMENT_21 has constant value "0" Constant
AMT_REQ_CREDIT_BUREAU_HOUR has constant value "0.0" Constant
AMT_REQ_CREDIT_BUREAU_DAY has constant value "0.0" Constant
AMT_REQ_CREDIT_BUREAU_WEEK has constant value "0.0" Constant
SK_ID_CURR is highly correlated with TARGET and 48 other fieldsHigh correlation
TARGET is highly correlated with SK_ID_CURR and 46 other fieldsHigh correlation
CNT_CHILDREN is highly correlated with AMT_CREDIT and 8 other fieldsHigh correlation
AMT_INCOME_TOTAL is highly correlated with AMT_CREDIT and 48 other fieldsHigh correlation
AMT_CREDIT is highly correlated with CNT_CHILDREN and 49 other fieldsHigh correlation
AMT_ANNUITY is highly correlated with AMT_INCOME_TOTAL and 47 other fieldsHigh correlation
AMT_GOODS_PRICE is highly correlated with AMT_INCOME_TOTAL and 46 other fieldsHigh correlation
REGION_POPULATION_RELATIVE is highly correlated with CNT_CHILDREN and 49 other fieldsHigh correlation
DAYS_BIRTH is highly correlated with TARGET and 47 other fieldsHigh correlation
DAYS_EMPLOYED is highly correlated with FLAG_EMP_PHONE and 43 other fieldsHigh correlation
DAYS_REGISTRATION is highly correlated with SK_ID_CURR and 44 other fieldsHigh correlation
DAYS_ID_PUBLISH is highly correlated with DAYS_REGISTRATION and 44 other fieldsHigh correlation
OWN_CAR_AGE is highly correlated with SK_ID_CURR and 16 other fieldsHigh correlation
FLAG_EMP_PHONE is highly correlated with DAYS_EMPLOYED and 2 other fieldsHigh correlation
FLAG_PHONE is highly correlated with SK_ID_CURR and 4 other fieldsHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDREN and 52 other fieldsHigh correlation
REGION_RATING_CLIENT is highly correlated with OWN_CAR_AGE and 44 other fieldsHigh correlation
REGION_RATING_CLIENT_W_CITY is highly correlated with OWN_CAR_AGE and 44 other fieldsHigh correlation
HOUR_APPR_PROCESS_START is highly correlated with AMT_ANNUITY and 45 other fieldsHigh correlation
REG_CITY_NOT_WORK_CITY is highly correlated with OWN_CAR_AGE and 4 other fieldsHigh correlation
LIVE_CITY_NOT_WORK_CITY is highly correlated with OWN_CAR_AGE and 4 other fieldsHigh correlation
EXT_SOURCE_1 is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
EXT_SOURCE_2 is highly correlated with DAYS_EMPLOYED and 43 other fieldsHigh correlation
EXT_SOURCE_3 is highly correlated with SK_ID_CURR and 8 other fieldsHigh correlation
APARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAPARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
APARTMENTS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
APARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAPARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
TOTALAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
OBS_30_CNT_SOCIAL_CIRCLE is highly correlated with AMT_INCOME_TOTAL and 45 other fieldsHigh correlation
DEF_30_CNT_SOCIAL_CIRCLE is highly correlated with SK_ID_CURR and 46 other fieldsHigh correlation
OBS_60_CNT_SOCIAL_CIRCLE is highly correlated with AMT_INCOME_TOTAL and 45 other fieldsHigh correlation
DEF_60_CNT_SOCIAL_CIRCLE is highly correlated with SK_ID_CURR and 46 other fieldsHigh correlation
DAYS_LAST_PHONE_CHANGE is highly correlated with REGION_POPULATION_RELATIVE and 44 other fieldsHigh correlation
FLAG_DOCUMENT_3 is highly correlated with OWN_CAR_AGE and 2 other fieldsHigh correlation
FLAG_DOCUMENT_8 is highly correlated with CNT_CHILDREN and 5 other fieldsHigh correlation
FLAG_DOCUMENT_14 is highly correlated with CNT_CHILDREN and 8 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_MON is highly correlated with CNT_CHILDREN and 9 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_QRT is highly correlated with CNT_CHILDREN and 9 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_YEAR is highly correlated with CNT_CHILDREN and 50 other fieldsHigh correlation
SK_ID_CURR is highly correlated with TARGET and 48 other fieldsHigh correlation
TARGET is highly correlated with SK_ID_CURR and 46 other fieldsHigh correlation
CNT_CHILDREN is highly correlated with AMT_CREDIT and 8 other fieldsHigh correlation
AMT_INCOME_TOTAL is highly correlated with AMT_CREDIT and 49 other fieldsHigh correlation
AMT_CREDIT is highly correlated with CNT_CHILDREN and 49 other fieldsHigh correlation
AMT_ANNUITY is highly correlated with AMT_INCOME_TOTAL and 48 other fieldsHigh correlation
AMT_GOODS_PRICE is highly correlated with AMT_INCOME_TOTAL and 46 other fieldsHigh correlation
REGION_POPULATION_RELATIVE is highly correlated with OWN_CAR_AGE and 45 other fieldsHigh correlation
DAYS_BIRTH is highly correlated with TARGET and 50 other fieldsHigh correlation
DAYS_EMPLOYED is highly correlated with CNT_CHILDREN and 49 other fieldsHigh correlation
DAYS_REGISTRATION is highly correlated with SK_ID_CURR and 45 other fieldsHigh correlation
DAYS_ID_PUBLISH is highly correlated with SK_ID_CURR and 49 other fieldsHigh correlation
OWN_CAR_AGE is highly correlated with SK_ID_CURR and 22 other fieldsHigh correlation
FLAG_EMP_PHONE is highly correlated with DAYS_BIRTH and 4 other fieldsHigh correlation
FLAG_PHONE is highly correlated with SK_ID_CURR and 6 other fieldsHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDREN and 52 other fieldsHigh correlation
REGION_RATING_CLIENT is highly correlated with OWN_CAR_AGE and 44 other fieldsHigh correlation
REGION_RATING_CLIENT_W_CITY is highly correlated with OWN_CAR_AGE and 44 other fieldsHigh correlation
HOUR_APPR_PROCESS_START is highly correlated with AMT_ANNUITY and 45 other fieldsHigh correlation
REG_CITY_NOT_WORK_CITY is highly correlated with OWN_CAR_AGE and 5 other fieldsHigh correlation
LIVE_CITY_NOT_WORK_CITY is highly correlated with OWN_CAR_AGE and 5 other fieldsHigh correlation
EXT_SOURCE_1 is highly correlated with SK_ID_CURR and 60 other fieldsHigh correlation
EXT_SOURCE_2 is highly correlated with DAYS_EMPLOYED and 44 other fieldsHigh correlation
EXT_SOURCE_3 is highly correlated with TARGET and 13 other fieldsHigh correlation
APARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAPARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
APARTMENTS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
APARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAPARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
TOTALAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
OBS_30_CNT_SOCIAL_CIRCLE is highly correlated with AMT_INCOME_TOTAL and 45 other fieldsHigh correlation
DEF_30_CNT_SOCIAL_CIRCLE is highly correlated with SK_ID_CURR and 46 other fieldsHigh correlation
OBS_60_CNT_SOCIAL_CIRCLE is highly correlated with AMT_INCOME_TOTAL and 45 other fieldsHigh correlation
DEF_60_CNT_SOCIAL_CIRCLE is highly correlated with SK_ID_CURR and 46 other fieldsHigh correlation
DAYS_LAST_PHONE_CHANGE is highly correlated with REGION_POPULATION_RELATIVE and 46 other fieldsHigh correlation
FLAG_DOCUMENT_3 is highly correlated with OWN_CAR_AGE and 2 other fieldsHigh correlation
FLAG_DOCUMENT_8 is highly correlated with CNT_CHILDREN and 6 other fieldsHigh correlation
FLAG_DOCUMENT_14 is highly correlated with CNT_CHILDREN and 8 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_MON is highly correlated with CNT_CHILDREN and 9 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_QRT is highly correlated with CNT_CHILDREN and 10 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_YEAR is highly correlated with CNT_CHILDREN and 49 other fieldsHigh correlation
SK_ID_CURR is highly correlated with DAYS_REGISTRATION and 44 other fieldsHigh correlation
TARGET is highly correlated with EXT_SOURCE_1 and 44 other fieldsHigh correlation
CNT_CHILDREN is highly correlated with CNT_FAM_MEMBERS and 6 other fieldsHigh correlation
AMT_INCOME_TOTAL is highly correlated with OWN_CAR_AGE and 43 other fieldsHigh correlation
AMT_CREDIT is highly correlated with AMT_ANNUITY and 44 other fieldsHigh correlation
AMT_ANNUITY is highly correlated with AMT_CREDIT and 46 other fieldsHigh correlation
AMT_GOODS_PRICE is highly correlated with AMT_CREDIT and 45 other fieldsHigh correlation
REGION_POPULATION_RELATIVE is highly correlated with APARTMENTS_AVG and 42 other fieldsHigh correlation
DAYS_BIRTH is highly correlated with EXT_SOURCE_3 and 41 other fieldsHigh correlation
DAYS_EMPLOYED is highly correlated with EXT_SOURCE_3 and 43 other fieldsHigh correlation
DAYS_REGISTRATION is highly correlated with SK_ID_CURR and 44 other fieldsHigh correlation
DAYS_ID_PUBLISH is highly correlated with DAYS_REGISTRATION and 42 other fieldsHigh correlation
OWN_CAR_AGE is highly correlated with SK_ID_CURR and 12 other fieldsHigh correlation
FLAG_EMP_PHONE is highly correlated with EXT_SOURCE_3High correlation
FLAG_PHONE is highly correlated with SK_ID_CURR and 5 other fieldsHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDREN and 49 other fieldsHigh correlation
REGION_RATING_CLIENT is highly correlated with OWN_CAR_AGE and 44 other fieldsHigh correlation
REGION_RATING_CLIENT_W_CITY is highly correlated with OWN_CAR_AGE and 44 other fieldsHigh correlation
HOUR_APPR_PROCESS_START is highly correlated with AMT_ANNUITY and 45 other fieldsHigh correlation
REG_CITY_NOT_WORK_CITY is highly correlated with OWN_CAR_AGE and 5 other fieldsHigh correlation
LIVE_CITY_NOT_WORK_CITY is highly correlated with OWN_CAR_AGE and 5 other fieldsHigh correlation
EXT_SOURCE_1 is highly correlated with SK_ID_CURR and 58 other fieldsHigh correlation
EXT_SOURCE_2 is highly correlated with APARTMENTS_AVG and 40 other fieldsHigh correlation
EXT_SOURCE_3 is highly correlated with TARGET and 6 other fieldsHigh correlation
APARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAPARTMENTS_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAREA_AVG is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
APARTMENTS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
APARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
BASEMENTAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BEGINEXPLUATATION_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
YEARS_BUILD_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
COMMONAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ELEVATORS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
ENTRANCES_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMAX_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
FLOORSMIN_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LANDAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAPARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
LIVINGAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAPARTMENTS_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
NONLIVINGAREA_MEDI is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
TOTALAREA_MODE is highly correlated with SK_ID_CURR and 62 other fieldsHigh correlation
OBS_30_CNT_SOCIAL_CIRCLE is highly correlated with AMT_INCOME_TOTAL and 44 other fieldsHigh correlation
DEF_30_CNT_SOCIAL_CIRCLE is highly correlated with TARGET and 44 other fieldsHigh correlation
OBS_60_CNT_SOCIAL_CIRCLE is highly correlated with AMT_INCOME_TOTAL and 44 other fieldsHigh correlation
DEF_60_CNT_SOCIAL_CIRCLE is highly correlated with TARGET and 44 other fieldsHigh correlation
DAYS_LAST_PHONE_CHANGE is highly correlated with APARTMENTS_AVG and 41 other fieldsHigh correlation
FLAG_DOCUMENT_3 is highly correlated with OWN_CAR_AGE and 2 other fieldsHigh correlation
FLAG_DOCUMENT_8 is highly correlated with CNT_CHILDREN and 5 other fieldsHigh correlation
FLAG_DOCUMENT_14 is highly correlated with CNT_CHILDREN and 6 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_MON is highly correlated with CNT_CHILDREN and 6 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_QRT is highly correlated with CNT_CHILDREN and 9 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_YEAR is highly correlated with CNT_CHILDREN and 48 other fieldsHigh correlation
WEEKDAY_APPR_PROCESS_START is highly correlated with AMT_ANNUITY and 18 other fieldsHigh correlation
AMT_ANNUITY is highly correlated with WEEKDAY_APPR_PROCESS_START and 23 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_YEAR is highly correlated with WEEKDAY_APPR_PROCESS_START and 15 other fieldsHigh correlation
OBS_60_CNT_SOCIAL_CIRCLE is highly correlated with NAME_FAMILY_STATUS and 13 other fieldsHigh correlation
FLAG_EMP_PHONE is highly correlated with AMT_ANNUITY and 11 other fieldsHigh correlation
OCCUPATION_TYPE is highly correlated with WEEKDAY_APPR_PROCESS_START and 22 other fieldsHigh correlation
NAME_FAMILY_STATUS is highly correlated with AMT_ANNUITY and 13 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_QRT is highly correlated with WEEKDAY_APPR_PROCESS_START and 10 other fieldsHigh correlation
REGION_RATING_CLIENT is highly correlated with OCCUPATION_TYPE and 12 other fieldsHigh correlation
NAME_TYPE_SUITE is highly correlated with AMT_ANNUITY and 16 other fieldsHigh correlation
DAYS_LAST_PHONE_CHANGE is highly correlated with AMT_ANNUITY and 18 other fieldsHigh correlation
DAYS_BIRTH is highly correlated with AMT_ANNUITY and 27 other fieldsHigh correlation
FLAG_WORK_PHONE is highly correlated with SK_ID_CURR and 2 other fieldsHigh correlation
CODE_GENDER is highly correlated with AMT_ANNUITY and 10 other fieldsHigh correlation
TARGET is highly correlated with DAYS_BIRTH and 7 other fieldsHigh correlation
HOUR_APPR_PROCESS_START is highly correlated with AMT_ANNUITY and 19 other fieldsHigh correlation
SK_ID_CURR is highly correlated with WEEKDAY_APPR_PROCESS_START and 42 other fieldsHigh correlation
CNT_FAM_MEMBERS is highly correlated with WEEKDAY_APPR_PROCESS_START and 14 other fieldsHigh correlation
FLAG_DOCUMENT_3 is highly correlated with WEEKDAY_APPR_PROCESS_START and 8 other fieldsHigh correlation
AMT_REQ_CREDIT_BUREAU_MON is highly correlated with WEEKDAY_APPR_PROCESS_START and 11 other fieldsHigh correlation
AMT_INCOME_TOTAL is highly correlated with WEEKDAY_APPR_PROCESS_START and 27 other fieldsHigh correlation
NAME_CONTRACT_TYPE is highly correlated with AMT_ANNUITY and 6 other fieldsHigh correlation
DAYS_EMPLOYED is highly correlated with AMT_ANNUITY and 11 other fieldsHigh correlation
EXT_SOURCE_3 is highly correlated with WEEKDAY_APPR_PROCESS_START and 32 other fieldsHigh correlation
FLAG_OWN_REALTY is highly correlated with AMT_ANNUITY and 12 other fieldsHigh correlation
DAYS_REGISTRATION is highly correlated with AMT_ANNUITY and 22 other fieldsHigh correlation
EXT_SOURCE_2 is highly correlated with AMT_ANNUITY and 25 other fieldsHigh correlation
EXT_SOURCE_1 is highly correlated with WEEKDAY_APPR_PROCESS_START and 40 other fieldsHigh correlation
DAYS_ID_PUBLISH is highly correlated with OBS_60_CNT_SOCIAL_CIRCLE and 17 other fieldsHigh correlation
CNT_CHILDREN is highly correlated with WEEKDAY_APPR_PROCESS_START and 9 other fieldsHigh correlation
REGION_POPULATION_RELATIVE is highly correlated with WEEKDAY_APPR_PROCESS_START and 16 other fieldsHigh correlation
FLAG_DOCUMENT_14 is highly correlated with WEEKDAY_APPR_PROCESS_START and 9 other fieldsHigh correlation
NAME_INCOME_TYPE is highly correlated with WEEKDAY_APPR_PROCESS_START and 24 other fieldsHigh correlation
FLAG_OWN_CAR is highly correlated with WEEKDAY_APPR_PROCESS_START and 7 other fieldsHigh correlation
DEF_60_CNT_SOCIAL_CIRCLE is highly correlated with DAYS_BIRTH and 7 other fieldsHigh correlation
REGION_RATING_CLIENT_W_CITY is highly correlated with OCCUPATION_TYPE and 12 other fieldsHigh correlation
OBS_30_CNT_SOCIAL_CIRCLE is highly correlated with OBS_60_CNT_SOCIAL_CIRCLE and 13 other fieldsHigh correlation
ORGANIZATION_TYPE is highly correlated with AMT_ANNUITY and 20 other fieldsHigh correlation
REG_CITY_NOT_WORK_CITY is highly correlated with OCCUPATION_TYPE and 4 other fieldsHigh correlation
AMT_CREDIT is highly correlated with AMT_ANNUITY and 23 other fieldsHigh correlation
AMT_GOODS_PRICE is highly correlated with AMT_ANNUITY and 23 other fieldsHigh correlation
FLAG_PHONE is highly correlated with AMT_ANNUITY and 5 other fieldsHigh correlation
DEF_30_CNT_SOCIAL_CIRCLE is highly correlated with DAYS_BIRTH and 7 other fieldsHigh correlation
LIVE_CITY_NOT_WORK_CITY is highly correlated with OCCUPATION_TYPE and 4 other fieldsHigh correlation
NAME_EDUCATION_TYPE is highly correlated with WEEKDAY_APPR_PROCESS_START and 11 other fieldsHigh correlation
OWN_CAR_AGE is highly correlated with WEEKDAY_APPR_PROCESS_START and 32 other fieldsHigh correlation
FLAG_DOCUMENT_8 is highly correlated with WEEKDAY_APPR_PROCESS_START and 6 other fieldsHigh correlation
OWN_CAR_AGE has 7 (70.0%) missing values Missing
OCCUPATION_TYPE has 1 (10.0%) missing values Missing
EXT_SOURCE_1 has 6 (60.0%) missing values Missing
EXT_SOURCE_3 has 4 (40.0%) missing values Missing
APARTMENTS_AVG has 8 (80.0%) missing values Missing
BASEMENTAREA_AVG has 8 (80.0%) missing values Missing
YEARS_BEGINEXPLUATATION_AVG has 8 (80.0%) missing values Missing
YEARS_BUILD_AVG has 8 (80.0%) missing values Missing
COMMONAREA_AVG has 8 (80.0%) missing values Missing
ELEVATORS_AVG has 8 (80.0%) missing values Missing
ENTRANCES_AVG has 8 (80.0%) missing values Missing
FLOORSMAX_AVG has 8 (80.0%) missing values Missing
FLOORSMIN_AVG has 8 (80.0%) missing values Missing
LANDAREA_AVG has 8 (80.0%) missing values Missing
LIVINGAPARTMENTS_AVG has 8 (80.0%) missing values Missing
LIVINGAREA_AVG has 8 (80.0%) missing values Missing
NONLIVINGAPARTMENTS_AVG has 8 (80.0%) missing values Missing
NONLIVINGAREA_AVG has 8 (80.0%) missing values Missing
APARTMENTS_MODE has 8 (80.0%) missing values Missing
BASEMENTAREA_MODE has 8 (80.0%) missing values Missing
YEARS_BEGINEXPLUATATION_MODE has 8 (80.0%) missing values Missing
YEARS_BUILD_MODE has 8 (80.0%) missing values Missing
COMMONAREA_MODE has 8 (80.0%) missing values Missing
ELEVATORS_MODE has 8 (80.0%) missing values Missing
ENTRANCES_MODE has 8 (80.0%) missing values Missing
FLOORSMAX_MODE has 8 (80.0%) missing values Missing
FLOORSMIN_MODE has 8 (80.0%) missing values Missing
LANDAREA_MODE has 8 (80.0%) missing values Missing
LIVINGAPARTMENTS_MODE has 8 (80.0%) missing values Missing
LIVINGAREA_MODE has 8 (80.0%) missing values Missing
NONLIVINGAPARTMENTS_MODE has 8 (80.0%) missing values Missing
NONLIVINGAREA_MODE has 8 (80.0%) missing values Missing
APARTMENTS_MEDI has 8 (80.0%) missing values Missing
BASEMENTAREA_MEDI has 8 (80.0%) missing values Missing
YEARS_BEGINEXPLUATATION_MEDI has 8 (80.0%) missing values Missing
YEARS_BUILD_MEDI has 8 (80.0%) missing values Missing
COMMONAREA_MEDI has 8 (80.0%) missing values Missing
ELEVATORS_MEDI has 8 (80.0%) missing values Missing
ENTRANCES_MEDI has 8 (80.0%) missing values Missing
FLOORSMAX_MEDI has 8 (80.0%) missing values Missing
FLOORSMIN_MEDI has 8 (80.0%) missing values Missing
LANDAREA_MEDI has 8 (80.0%) missing values Missing
LIVINGAPARTMENTS_MEDI has 8 (80.0%) missing values Missing
LIVINGAREA_MEDI has 8 (80.0%) missing values Missing
NONLIVINGAPARTMENTS_MEDI has 8 (80.0%) missing values Missing
NONLIVINGAREA_MEDI has 8 (80.0%) missing values Missing
FONDKAPREMONT_MODE has 8 (80.0%) missing values Missing
HOUSETYPE_MODE has 8 (80.0%) missing values Missing
TOTALAREA_MODE has 8 (80.0%) missing values Missing
WALLSMATERIAL_MODE has 8 (80.0%) missing values Missing
EMERGENCYSTATE_MODE has 8 (80.0%) missing values Missing
AMT_REQ_CREDIT_BUREAU_HOUR has 2 (20.0%) missing values Missing
AMT_REQ_CREDIT_BUREAU_DAY has 2 (20.0%) missing values Missing
AMT_REQ_CREDIT_BUREAU_WEEK has 2 (20.0%) missing values Missing
AMT_REQ_CREDIT_BUREAU_MON has 2 (20.0%) missing values Missing
AMT_REQ_CREDIT_BUREAU_QRT has 2 (20.0%) missing values Missing
AMT_REQ_CREDIT_BUREAU_YEAR has 2 (20.0%) missing values Missing
OWN_CAR_AGE is uniformly distributed Uniform
FLAG_PHONE is uniformly distributed Uniform
EXT_SOURCE_1 is uniformly distributed Uniform
APARTMENTS_AVG is uniformly distributed Uniform
BASEMENTAREA_AVG is uniformly distributed Uniform
YEARS_BEGINEXPLUATATION_AVG is uniformly distributed Uniform
YEARS_BUILD_AVG is uniformly distributed Uniform
COMMONAREA_AVG is uniformly distributed Uniform
ELEVATORS_AVG is uniformly distributed Uniform
ENTRANCES_AVG is uniformly distributed Uniform
FLOORSMAX_AVG is uniformly distributed Uniform
FLOORSMIN_AVG is uniformly distributed Uniform
LANDAREA_AVG is uniformly distributed Uniform
LIVINGAPARTMENTS_AVG is uniformly distributed Uniform
LIVINGAREA_AVG is uniformly distributed Uniform
NONLIVINGAPARTMENTS_AVG is uniformly distributed Uniform
NONLIVINGAREA_AVG is uniformly distributed Uniform
APARTMENTS_MODE is uniformly distributed Uniform
BASEMENTAREA_MODE is uniformly distributed Uniform
YEARS_BEGINEXPLUATATION_MODE is uniformly distributed Uniform
YEARS_BUILD_MODE is uniformly distributed Uniform
COMMONAREA_MODE is uniformly distributed Uniform
ELEVATORS_MODE is uniformly distributed Uniform
ENTRANCES_MODE is uniformly distributed Uniform
FLOORSMAX_MODE is uniformly distributed Uniform
FLOORSMIN_MODE is uniformly distributed Uniform
LANDAREA_MODE is uniformly distributed Uniform
LIVINGAPARTMENTS_MODE is uniformly distributed Uniform
LIVINGAREA_MODE is uniformly distributed Uniform
APARTMENTS_MEDI is uniformly distributed Uniform
BASEMENTAREA_MEDI is uniformly distributed Uniform
YEARS_BEGINEXPLUATATION_MEDI is uniformly distributed Uniform
YEARS_BUILD_MEDI is uniformly distributed Uniform
COMMONAREA_MEDI is uniformly distributed Uniform
ELEVATORS_MEDI is uniformly distributed Uniform
ENTRANCES_MEDI is uniformly distributed Uniform
FLOORSMAX_MEDI is uniformly distributed Uniform
FLOORSMIN_MEDI is uniformly distributed Uniform
LANDAREA_MEDI is uniformly distributed Uniform
LIVINGAPARTMENTS_MEDI is uniformly distributed Uniform
LIVINGAREA_MEDI is uniformly distributed Uniform
NONLIVINGAPARTMENTS_MEDI is uniformly distributed Uniform
NONLIVINGAREA_MEDI is uniformly distributed Uniform
TOTALAREA_MODE is uniformly distributed Uniform
WALLSMATERIAL_MODE is uniformly distributed Uniform
SK_ID_CURR has unique values Unique
AMT_CREDIT has unique values Unique
AMT_ANNUITY has unique values Unique
AMT_GOODS_PRICE has unique values Unique
DAYS_BIRTH has unique values Unique
DAYS_EMPLOYED has unique values Unique
DAYS_REGISTRATION has unique values Unique
DAYS_ID_PUBLISH has unique values Unique
EXT_SOURCE_2 has unique values Unique
DAYS_LAST_PHONE_CHANGE has unique values Unique
DAYS_LAST_PHONE_CHANGE has 1 (10.0%) zeros Zeros

Reproduction

Analysis started2021-09-09 11:59:39.907465
Analysis finished2021-09-09 12:00:03.522086
Duration23.61 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

SK_ID_CURR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100007.2
Minimum100002
Maximum100012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:03.571780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100002
5-th percentile100002.45
Q1100004.5
median100007.5
Q3100009.75
95-th percentile100011.55
Maximum100012
Range10
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation3.425395354
Coefficient of variation (CV)3.425148744 × 10-5
Kurtosis-1.23046875
Mean100007.2
Median Absolute Deviation (MAD)3
Skewness-0.1915837245
Sum1000072
Variance11.73333333
MonotonicityStrictly increasing
2021-09-09T20:00:03.635475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1000121
10.0%
1000111
10.0%
1000101
10.0%
1000091
10.0%
1000081
10.0%
1000071
10.0%
1000061
10.0%
1000041
10.0%
1000031
10.0%
1000021
10.0%
ValueCountFrequency (%)
1000021
10.0%
1000031
10.0%
1000041
10.0%
1000061
10.0%
1000071
10.0%
1000081
10.0%
1000091
10.0%
1000101
10.0%
1000111
10.0%
1000121
10.0%
ValueCountFrequency (%)
1000121
10.0%
1000111
10.0%
1000101
10.0%
1000091
10.0%
1000081
10.0%
1000071
10.0%
1000061
10.0%
1000041
10.0%
1000031
10.0%
1000021
10.0%

TARGET
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Length

2021-09-09T20:00:03.770352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:03.802126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring characters

ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

NAME_CONTRACT_TYPE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size808.0 B
Cash loans
Revolving loans

Length

Max length15
Median length10
Mean length11
Min length10

Characters and Unicode

Total characters110
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash loans
2nd rowCash loans
3rd rowRevolving loans
4th rowCash loans
5th rowCash loans

Common Values

ValueCountFrequency (%)
Cash loans8
80.0%
Revolving loans2
 
20.0%

Length

2021-09-09T20:00:04.245194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:04.287659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
loans10
50.0%
cash8
40.0%
revolving2
 
10.0%

Most occurring characters

ValueCountFrequency (%)
a18
16.4%
s18
16.4%
l12
10.9%
o12
10.9%
n12
10.9%
10
9.1%
C8
7.3%
h8
7.3%
v4
 
3.6%
R2
 
1.8%
Other values (3)6
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter90
81.8%
Uppercase Letter10
 
9.1%
Space Separator10
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a18
20.0%
s18
20.0%
l12
13.3%
o12
13.3%
n12
13.3%
h8
8.9%
v4
 
4.4%
e2
 
2.2%
i2
 
2.2%
g2
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
C8
80.0%
R2
 
20.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100
90.9%
Common10
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a18
18.0%
s18
18.0%
l12
12.0%
o12
12.0%
n12
12.0%
C8
8.0%
h8
8.0%
v4
 
4.0%
R2
 
2.0%
e2
 
2.0%
Other values (2)4
 
4.0%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a18
16.4%
s18
16.4%
l12
10.9%
o12
10.9%
n12
10.9%
10
9.1%
C8
7.3%
h8
7.3%
v4
 
3.6%
R2
 
1.8%
Other values (3)6
 
5.5%

CODE_GENDER
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
M
F

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M6
60.0%
F4
40.0%

Length

2021-09-09T20:00:04.401412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:04.439212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
m6
60.0%
f4
40.0%

Most occurring characters

ValueCountFrequency (%)
M6
60.0%
F4
40.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M6
60.0%
F4
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M6
60.0%
F4
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M6
60.0%
F4
40.0%

FLAG_OWN_CAR
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size138.0 B
False
True
ValueCountFrequency (%)
False7
70.0%
True3
30.0%
2021-09-09T20:00:04.462151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

FLAG_OWN_REALTY
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size138.0 B
True
False
ValueCountFrequency (%)
True9
90.0%
False1
 
10.0%
2021-09-09T20:00:04.485881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

CNT_CHILDREN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Length

2021-09-09T20:00:04.594676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:04.632813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring characters

ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

AMT_INCOME_TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167400
Minimum67500
Maximum360000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:04.667754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum67500
5-th percentile81675
Q1114750
median135000
Q3194625
95-th percentile319500
Maximum360000
Range292500
Interquartile range (IQR)79875

Descriptive statistics

Standard deviation88660.02481
Coefficient of variation (CV)0.5296297779
Kurtosis1.402619048
Mean167400
Median Absolute Deviation (MAD)36000
Skewness1.319409462
Sum1674000
Variance7860600000
MonotonicityNot monotonic
2021-09-09T20:00:04.721063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1350002
20.0%
2700001
10.0%
675001
10.0%
990001
10.0%
1125001
10.0%
1215001
10.0%
3600001
10.0%
1710001
10.0%
2025001
10.0%
ValueCountFrequency (%)
675001
10.0%
990001
10.0%
1125001
10.0%
1215001
10.0%
1350002
20.0%
1710001
10.0%
2025001
10.0%
2700001
10.0%
3600001
10.0%
ValueCountFrequency (%)
3600001
10.0%
2700001
10.0%
2025001
10.0%
1710001
10.0%
1350002
20.0%
1215001
10.0%
1125001
10.0%
990001
10.0%
675001
10.0%

AMT_CREDIT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean766661.4
Minimum135000
Maximum1560726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:04.779358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum135000
5-th percentile214957.125
Q1405399.375
median501747.75
Q31225029.375
95-th percentile1546899.3
Maximum1560726
Range1425726
Interquartile range (IQR)819630

Descriptive statistics

Standard deviation533429.0125
Coefficient of variation (CV)0.6957817526
Kurtosis-1.47175088
Mean766661.4
Median Absolute Deviation (MAD)277906.5
Skewness0.5773194265
Sum7666614
Variance2.845465114 × 1011
MonotonicityNot monotonic
2021-09-09T20:00:04.835825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1293502.51
10.0%
490495.51
10.0%
15607261
10.0%
406597.51
10.0%
10196101
10.0%
15300001
10.0%
5130001
10.0%
312682.51
10.0%
4050001
10.0%
1350001
10.0%
ValueCountFrequency (%)
1350001
10.0%
312682.51
10.0%
4050001
10.0%
406597.51
10.0%
490495.51
10.0%
5130001
10.0%
10196101
10.0%
1293502.51
10.0%
15300001
10.0%
15607261
10.0%
ValueCountFrequency (%)
15607261
10.0%
15300001
10.0%
1293502.51
10.0%
10196101
10.0%
5130001
10.0%
490495.51
10.0%
406597.51
10.0%
4050001
10.0%
312682.51
10.0%
1350001
10.0%

AMT_ANNUITY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28367.1
Minimum6750
Maximum42075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:04.897251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6750
5-th percentile12825
Q122574.25
median28602
Q335230.5
95-th percentile41726.7
Maximum42075
Range35325
Interquartile range (IQR)12656.25

Descriptive statistics

Standard deviation10698.44477
Coefficient of variation (CV)0.3771427032
Kurtosis0.5213427299
Mean28367.1
Median Absolute Deviation (MAD)6916.5
Skewness-0.6422828635
Sum283671
Variance114456720.6
MonotonicityNot monotonic
2021-09-09T20:00:04.955230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
27517.51
10.0%
33826.51
10.0%
29686.51
10.0%
202501
10.0%
35698.51
10.0%
21865.51
10.0%
413011
10.0%
420751
10.0%
67501
10.0%
24700.51
10.0%
ValueCountFrequency (%)
67501
10.0%
202501
10.0%
21865.51
10.0%
24700.51
10.0%
27517.51
10.0%
29686.51
10.0%
33826.51
10.0%
35698.51
10.0%
413011
10.0%
420751
10.0%
ValueCountFrequency (%)
420751
10.0%
413011
10.0%
35698.51
10.0%
33826.51
10.0%
29686.51
10.0%
27517.51
10.0%
24700.51
10.0%
21865.51
10.0%
202501
10.0%
67501
10.0%

AMT_GOODS_PRICE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean712350
Minimum135000
Maximum1530000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:05.017336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum135000
5-th percentile207900
Q1364500
median483750
Q31075500
95-th percentile1469250
Maximum1530000
Range1395000
Interquartile range (IQR)711000

Descriptive statistics

Standard deviation492854.6692
Coefficient of variation (CV)0.6918715087
Kurtosis-1.125241744
Mean712350
Median Absolute Deviation (MAD)267750
Skewness0.6730048923
Sum7123500
Variance2.42905725 × 1011
MonotonicityNot monotonic
2021-09-09T20:00:05.072092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11295001
10.0%
1350001
10.0%
13950001
10.0%
2970001
10.0%
4545001
10.0%
9135001
10.0%
5130001
10.0%
4050001
10.0%
15300001
10.0%
3510001
10.0%
ValueCountFrequency (%)
1350001
10.0%
2970001
10.0%
3510001
10.0%
4050001
10.0%
4545001
10.0%
5130001
10.0%
9135001
10.0%
11295001
10.0%
13950001
10.0%
15300001
10.0%
ValueCountFrequency (%)
15300001
10.0%
13950001
10.0%
11295001
10.0%
9135001
10.0%
5130001
10.0%
4545001
10.0%
4050001
10.0%
3510001
10.0%
2970001
10.0%
1350001
10.0%

NAME_TYPE_SUITE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size818.0 B
Unaccompanied
Spouse, partner
Family
Children

Length

Max length15
Median length13
Mean length12
Min length6

Characters and Unicode

Total characters120
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st rowUnaccompanied
2nd rowFamily
3rd rowUnaccompanied
4th rowUnaccompanied
5th rowUnaccompanied

Common Values

ValueCountFrequency (%)
Unaccompanied7
70.0%
Spouse, partner1
 
10.0%
Family1
 
10.0%
Children1
 
10.0%

Length

2021-09-09T20:00:05.205459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:05.248714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
unaccompanied7
63.6%
family1
 
9.1%
children1
 
9.1%
partner1
 
9.1%
spouse1
 
9.1%

Most occurring characters

ValueCountFrequency (%)
n16
13.3%
a16
13.3%
c14
11.7%
e10
8.3%
p9
7.5%
i9
7.5%
o8
6.7%
m8
6.7%
d8
6.7%
U7
5.8%
Other values (12)15
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter108
90.0%
Uppercase Letter10
 
8.3%
Other Punctuation1
 
0.8%
Space Separator1
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n16
14.8%
a16
14.8%
c14
13.0%
e10
9.3%
p9
8.3%
i9
8.3%
o8
7.4%
m8
7.4%
d8
7.4%
r3
 
2.8%
Other values (6)7
6.5%
Uppercase Letter
ValueCountFrequency (%)
U7
70.0%
F1
 
10.0%
S1
 
10.0%
C1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
,1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin118
98.3%
Common2
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n16
13.6%
a16
13.6%
c14
11.9%
e10
8.5%
p9
7.6%
i9
7.6%
o8
6.8%
m8
6.8%
d8
6.8%
U7
5.9%
Other values (10)13
11.0%
Common
ValueCountFrequency (%)
,1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n16
13.3%
a16
13.3%
c14
11.7%
e10
8.3%
p9
7.5%
i9
7.5%
o8
6.7%
m8
6.7%
d8
6.7%
U7
5.8%
Other values (12)15
12.5%

NAME_INCOME_TYPE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size801.0 B
Working
State servant
Pensioner
Commercial associate

Length

Max length20
Median length8
Mean length10.3
Min length7

Characters and Unicode

Total characters103
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st rowWorking
2nd rowState servant
3rd rowWorking
4th rowWorking
5th rowWorking

Common Values

ValueCountFrequency (%)
Working5
50.0%
State servant3
30.0%
Pensioner1
 
10.0%
Commercial associate1
 
10.0%

Length

2021-09-09T20:00:05.381768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:05.426260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
working5
35.7%
state3
21.4%
servant3
21.4%
pensioner1
 
7.1%
associate1
 
7.1%
commercial1
 
7.1%

Most occurring characters

ValueCountFrequency (%)
r10
9.7%
n10
9.7%
t10
9.7%
e10
9.7%
a9
8.7%
o8
 
7.8%
i8
 
7.8%
s6
 
5.8%
W5
 
4.9%
k5
 
4.9%
Other values (9)22
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter89
86.4%
Uppercase Letter10
 
9.7%
Space Separator4
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r10
11.2%
n10
11.2%
t10
11.2%
e10
11.2%
a9
10.1%
o8
9.0%
i8
9.0%
s6
6.7%
k5
5.6%
g5
5.6%
Other values (4)8
9.0%
Uppercase Letter
ValueCountFrequency (%)
W5
50.0%
S3
30.0%
C1
 
10.0%
P1
 
10.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin99
96.1%
Common4
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r10
10.1%
n10
10.1%
t10
10.1%
e10
10.1%
a9
9.1%
o8
8.1%
i8
8.1%
s6
 
6.1%
W5
 
5.1%
k5
 
5.1%
Other values (8)18
18.2%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r10
9.7%
n10
9.7%
t10
9.7%
e10
9.7%
a9
8.7%
o8
 
7.8%
i8
 
7.8%
s6
 
5.8%
W5
 
4.9%
k5
 
4.9%
Other values (9)22
21.4%

NAME_EDUCATION_TYPE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size949.0 B
Secondary / secondary special
Higher education

Length

Max length29
Median length29
Mean length25.1
Min length16

Characters and Unicode

Total characters251
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary / secondary special
2nd rowHigher education
3rd rowSecondary / secondary special
4th rowSecondary / secondary special
5th rowSecondary / secondary special

Common Values

ValueCountFrequency (%)
Secondary / secondary special7
70.0%
Higher education3
30.0%

Length

2021-09-09T20:00:05.553614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:05.596975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
secondary14
41.2%
special7
20.6%
7
20.6%
higher3
 
8.8%
education3
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e27
10.8%
c24
9.6%
a24
9.6%
24
9.6%
o17
 
6.8%
n17
 
6.8%
d17
 
6.8%
r17
 
6.8%
y14
 
5.6%
s14
 
5.6%
Other values (10)56
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter210
83.7%
Space Separator24
 
9.6%
Uppercase Letter10
 
4.0%
Other Punctuation7
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e27
12.9%
c24
11.4%
a24
11.4%
o17
8.1%
n17
8.1%
d17
8.1%
r17
8.1%
y14
6.7%
s14
6.7%
i13
6.2%
Other values (6)26
12.4%
Uppercase Letter
ValueCountFrequency (%)
S7
70.0%
H3
30.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
/7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin220
87.6%
Common31
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e27
12.3%
c24
10.9%
a24
10.9%
o17
7.7%
n17
7.7%
d17
7.7%
r17
7.7%
y14
 
6.4%
s14
 
6.4%
i13
 
5.9%
Other values (8)36
16.4%
Common
ValueCountFrequency (%)
24
77.4%
/7
 
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e27
10.8%
c24
9.6%
a24
9.6%
24
9.6%
o17
 
6.8%
n17
 
6.8%
d17
 
6.8%
r17
 
6.8%
y14
 
5.6%
s14
 
5.6%
Other values (10)56
22.3%

NAME_FAMILY_STATUS
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size827.0 B
Married
Single / not married
Civil marriage

Length

Max length20
Median length10.5
Mean length12.9
Min length7

Characters and Unicode

Total characters129
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st rowSingle / not married
2nd rowMarried
3rd rowSingle / not married
4th rowCivil marriage
5th rowSingle / not married

Common Values

ValueCountFrequency (%)
Married5
50.0%
Single / not married4
40.0%
Civil marriage1
 
10.0%

Length

2021-09-09T20:00:05.721262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:05.764017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married9
39.1%
not4
17.4%
single4
17.4%
4
17.4%
civil1
 
4.3%
marriage1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
r20
15.5%
i16
12.4%
e14
10.9%
13
10.1%
a11
8.5%
d9
 
7.0%
n8
 
6.2%
g5
 
3.9%
l5
 
3.9%
m5
 
3.9%
Other values (7)23
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter102
79.1%
Space Separator13
 
10.1%
Uppercase Letter10
 
7.8%
Other Punctuation4
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r20
19.6%
i16
15.7%
e14
13.7%
a11
10.8%
d9
8.8%
n8
 
7.8%
g5
 
4.9%
l5
 
4.9%
m5
 
4.9%
o4
 
3.9%
Other values (2)5
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
M5
50.0%
S4
40.0%
C1
 
10.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
/4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin112
86.8%
Common17
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r20
17.9%
i16
14.3%
e14
12.5%
a11
9.8%
d9
8.0%
n8
 
7.1%
g5
 
4.5%
l5
 
4.5%
m5
 
4.5%
M5
 
4.5%
Other values (5)14
12.5%
Common
ValueCountFrequency (%)
13
76.5%
/4
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r20
15.5%
i16
12.4%
e14
10.9%
13
10.1%
a11
8.5%
d9
 
7.0%
n8
 
6.2%
g5
 
3.9%
l5
 
3.9%
m5
 
3.9%
Other values (7)23
17.8%

NAME_HOUSING_TYPE
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
House / apartment
10 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters170
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse / apartment
2nd rowHouse / apartment
3rd rowHouse / apartment
4th rowHouse / apartment
5th rowHouse / apartment

Common Values

ValueCountFrequency (%)
House / apartment10
100.0%

Length

2021-09-09T20:00:05.870012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:05.908479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
apartment10
33.3%
house10
33.3%
10
33.3%

Most occurring characters

ValueCountFrequency (%)
e20
11.8%
20
11.8%
a20
11.8%
t20
11.8%
H10
 
5.9%
o10
 
5.9%
u10
 
5.9%
s10
 
5.9%
/10
 
5.9%
p10
 
5.9%
Other values (3)30
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter130
76.5%
Space Separator20
 
11.8%
Uppercase Letter10
 
5.9%
Other Punctuation10
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e20
15.4%
a20
15.4%
t20
15.4%
o10
7.7%
u10
7.7%
s10
7.7%
p10
7.7%
r10
7.7%
m10
7.7%
n10
7.7%
Uppercase Letter
ValueCountFrequency (%)
H10
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
/10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin140
82.4%
Common30
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e20
14.3%
a20
14.3%
t20
14.3%
H10
7.1%
o10
7.1%
u10
7.1%
s10
7.1%
p10
7.1%
r10
7.1%
m10
7.1%
Common
ValueCountFrequency (%)
20
66.7%
/10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e20
11.8%
20
11.8%
a20
11.8%
t20
11.8%
H10
 
5.9%
o10
 
5.9%
u10
 
5.9%
s10
 
5.9%
/10
 
5.9%
p10
 
5.9%
Other values (3)30
17.6%

REGION_POPULATION_RELATIVE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0182085
Minimum0.003122
Maximum0.035792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:05.942170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.003122
5-th percentile0.00331055
Q10.00852225
median0.0187175
Q30.0264195
95-th percentile0.035792
Maximum0.035792
Range0.03267
Interquartile range (IQR)0.01789725

Descriptive statistics

Standard deviation0.01221928622
Coefficient of variation (CV)0.6710759384
Kurtosis-1.241070031
Mean0.0182085
Median Absolute Deviation (MAD)0.010322
Skewness0.2912015997
Sum0.182085
Variance0.0001493109558
MonotonicityNot monotonic
2021-09-09T20:00:05.996593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0357922
20.0%
0.0100321
10.0%
0.0035411
10.0%
0.0286631
10.0%
0.0031221
10.0%
0.0188011
10.0%
0.0186341
10.0%
0.0196891
10.0%
0.0080191
10.0%
ValueCountFrequency (%)
0.0031221
10.0%
0.0035411
10.0%
0.0080191
10.0%
0.0100321
10.0%
0.0186341
10.0%
0.0188011
10.0%
0.0196891
10.0%
0.0286631
10.0%
0.0357922
20.0%
ValueCountFrequency (%)
0.0357922
20.0%
0.0286631
10.0%
0.0196891
10.0%
0.0188011
10.0%
0.0186341
10.0%
0.0100321
10.0%
0.0080191
10.0%
0.0035411
10.0%
0.0031221
10.0%

DAYS_BIRTH
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-16834.6
Minimum-20099
Maximum-9461
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)100.0%
Memory size208.0 B
2021-09-09T20:00:06.057278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-20099
5-th percentile-20023.85
Q1-19035.75
median-17895.5
Q3-15043
95-th percentile-11403.65
Maximum-9461
Range10638
Interquartile range (IQR)3992.75

Descriptive statistics

Standard deviation3386.869521
Coefficient of variation (CV)-0.2011850309
Kurtosis1.205590712
Mean-16834.6
Median Absolute Deviation (MAD)1593.5
Skewness1.247555439
Sum-168346
Variance11470885.16
MonotonicityNot monotonic
2021-09-09T20:00:06.116212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-188501
10.0%
-167651
10.0%
-199321
10.0%
-137781
10.0%
-144691
10.0%
-190461
10.0%
-94611
10.0%
-190051
10.0%
-200991
10.0%
-169411
10.0%
ValueCountFrequency (%)
-200991
10.0%
-199321
10.0%
-190461
10.0%
-190051
10.0%
-188501
10.0%
-169411
10.0%
-167651
10.0%
-144691
10.0%
-137781
10.0%
-94611
10.0%
ValueCountFrequency (%)
-94611
10.0%
-137781
10.0%
-144691
10.0%
-167651
10.0%
-169411
10.0%
-188501
10.0%
-190051
10.0%
-190461
10.0%
-199321
10.0%
-200991
10.0%

DAYS_EMPLOYED
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34993
Minimum-3130
Maximum365243
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)90.0%
Memory size208.0 B
2021-09-09T20:00:06.173741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3130
5-th percentile-3089.05
Q1-2783.25
median-1388
Q3-496
95-th percentile200782.4
Maximum365243
Range368373
Interquartile range (IQR)2287.25

Descriptive statistics

Standard deviation116043.2285
Coefficient of variation (CV)3.31618405
Kurtosis9.997532692
Mean34993
Median Absolute Deviation (MAD)1051
Skewness3.161745518
Sum349930
Variance1.346603087 × 1010
MonotonicityNot monotonic
2021-09-09T20:00:06.226884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-20191
10.0%
-30391
10.0%
-30381
10.0%
-6371
10.0%
-4491
10.0%
3652431
10.0%
-31301
10.0%
-11881
10.0%
-15881
10.0%
-2251
10.0%
ValueCountFrequency (%)
-31301
10.0%
-30391
10.0%
-30381
10.0%
-20191
10.0%
-15881
10.0%
-11881
10.0%
-6371
10.0%
-4491
10.0%
-2251
10.0%
3652431
10.0%
ValueCountFrequency (%)
3652431
10.0%
-2251
10.0%
-4491
10.0%
-6371
10.0%
-11881
10.0%
-15881
10.0%
-20191
10.0%
-30381
10.0%
-30391
10.0%
-31301
10.0%

DAYS_REGISTRATION
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5588.2
Minimum-14437
Maximum-1186
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)100.0%
Memory size208.0 B
2021-09-09T20:00:06.283445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-14437
5-th percentile-12365.2
Q1-6812.75
median-4454
Q3-3801
95-th percentile-1198.15
Maximum-1186
Range13251
Interquartile range (IQR)3011.75

Descriptive statistics

Standard deviation4037.971075
Coefficient of variation (CV)-0.7225888613
Kurtosis1.596266945
Mean-5588.2
Median Absolute Deviation (MAD)1889.5
Skewness-1.252131009
Sum-55882
Variance16305210.4
MonotonicityNot monotonic
2021-09-09T20:00:06.346702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-144371
10.0%
-98331
10.0%
-74271
10.0%
-45971
10.0%
-12131
10.0%
-49701
10.0%
-43111
10.0%
-42601
10.0%
-11861
10.0%
-36481
10.0%
ValueCountFrequency (%)
-144371
10.0%
-98331
10.0%
-74271
10.0%
-49701
10.0%
-45971
10.0%
-43111
10.0%
-42601
10.0%
-36481
10.0%
-12131
10.0%
-11861
10.0%
ValueCountFrequency (%)
-11861
10.0%
-12131
10.0%
-36481
10.0%
-42601
10.0%
-43111
10.0%
-45971
10.0%
-49701
10.0%
-74271
10.0%
-98331
10.0%
-144371
10.0%

DAYS_ID_PUBLISH
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2181.8
Minimum-3992
Maximum-291
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)100.0%
Memory size208.0 B
2021-09-09T20:00:06.407583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3992
5-th percentile-3776.9
Q1-3226.25
median-2408
Q3-994.25
95-th percentile-374.7
Maximum-291
Range3701
Interquartile range (IQR)2232

Descriptive statistics

Standard deviation1326.392501
Coefficient of variation (CV)-0.6079349624
Kurtosis-1.254027435
Mean-2181.8
Median Absolute Deviation (MAD)1078
Skewness0.3029648975
Sum-21818
Variance1759317.067
MonotonicityNot monotonic
2021-09-09T20:00:06.463713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-25311
10.0%
-39921
10.0%
-4771
10.0%
-6191
10.0%
-35141
10.0%
-21201
10.0%
-24371
10.0%
-23791
10.0%
-2911
10.0%
-34581
10.0%
ValueCountFrequency (%)
-39921
10.0%
-35141
10.0%
-34581
10.0%
-25311
10.0%
-24371
10.0%
-23791
10.0%
-21201
10.0%
-6191
10.0%
-4771
10.0%
-2911
10.0%
ValueCountFrequency (%)
-2911
10.0%
-4771
10.0%
-6191
10.0%
-21201
10.0%
-23791
10.0%
-24371
10.0%
-25311
10.0%
-34581
10.0%
-35141
10.0%
-39921
10.0%

OWN_CAR_AGE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing7
Missing (%)70.0%
Memory size590.0 B
26.0
8.0
17.0

Length

Max length4
Median length4
Mean length3.666666667
Min length3

Characters and Unicode

Total characters11
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row26.0
2nd row17.0
3rd row8.0

Common Values

ValueCountFrequency (%)
26.01
 
10.0%
8.01
 
10.0%
17.01
 
10.0%
(Missing)7
70.0%

Length

2021-09-09T20:00:06.593172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:06.637667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
17.01
33.3%
8.01
33.3%
26.01
33.3%

Most occurring characters

ValueCountFrequency (%)
.3
27.3%
03
27.3%
21
 
9.1%
61
 
9.1%
11
 
9.1%
71
 
9.1%
81
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8
72.7%
Other Punctuation3
 
27.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
37.5%
21
 
12.5%
61
 
12.5%
11
 
12.5%
71
 
12.5%
81
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3
27.3%
03
27.3%
21
 
9.1%
61
 
9.1%
11
 
9.1%
71
 
9.1%
81
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3
27.3%
03
27.3%
21
 
9.1%
61
 
9.1%
11
 
9.1%
71
 
9.1%
81
 
9.1%

FLAG_MOBIL
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110
100.0%

Length

2021-09-09T20:00:06.741120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:06.778525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
110
100.0%

Most occurring characters

ValueCountFrequency (%)
110
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
100.0%

FLAG_EMP_PHONE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
1
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
19
90.0%
01
 
10.0%

Length

2021-09-09T20:00:06.883013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:06.920909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
19
90.0%
01
 
10.0%

Most occurring characters

ValueCountFrequency (%)
19
90.0%
01
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
19
90.0%
01
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
19
90.0%
01
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19
90.0%
01
 
10.0%

FLAG_WORK_PHONE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07
70.0%
13
30.0%

Length

2021-09-09T20:00:07.031167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.068966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07
70.0%
13
30.0%

Most occurring characters

ValueCountFrequency (%)
07
70.0%
13
30.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07
70.0%
13
30.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07
70.0%
13
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07
70.0%
13
30.0%

FLAG_CONT_MOBILE
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110
100.0%

Length

2021-09-09T20:00:07.166845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.204188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
110
100.0%

Most occurring characters

ValueCountFrequency (%)
110
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
100.0%

FLAG_PHONE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
1
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
15
50.0%
05
50.0%

Length

2021-09-09T20:00:07.304568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.342477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05
50.0%
15
50.0%

Most occurring characters

ValueCountFrequency (%)
15
50.0%
05
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15
50.0%
05
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15
50.0%
05
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
50.0%
05
50.0%

FLAG_EMAIL
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:07.440559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.477824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

OCCUPATION_TYPE
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)44.4%
Missing1
Missing (%)10.0%
Memory size752.0 B
Laborers
Core staff
Accountants
Managers

Length

Max length11
Median length8
Mean length8.777777778
Min length8

Characters and Unicode

Total characters79
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)22.2%

Sample

1st rowLaborers
2nd rowCore staff
3rd rowLaborers
4th rowLaborers
5th rowCore staff

Common Values

ValueCountFrequency (%)
Laborers5
50.0%
Core staff2
 
20.0%
Accountants1
 
10.0%
Managers1
 
10.0%
(Missing)1
 
10.0%

Length

2021-09-09T20:00:07.595278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.640028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
laborers5
45.5%
core2
 
18.2%
staff2
 
18.2%
accountants1
 
9.1%
managers1
 
9.1%

Most occurring characters

ValueCountFrequency (%)
r13
16.5%
a10
12.7%
s9
11.4%
o8
10.1%
e8
10.1%
L5
 
6.3%
b5
 
6.3%
t4
 
5.1%
f4
 
5.1%
n3
 
3.8%
Other values (7)10
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter68
86.1%
Uppercase Letter9
 
11.4%
Space Separator2
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r13
19.1%
a10
14.7%
s9
13.2%
o8
11.8%
e8
11.8%
b5
 
7.4%
t4
 
5.9%
f4
 
5.9%
n3
 
4.4%
c2
 
2.9%
Other values (2)2
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
L5
55.6%
C2
 
22.2%
A1
 
11.1%
M1
 
11.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin77
97.5%
Common2
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r13
16.9%
a10
13.0%
s9
11.7%
o8
10.4%
e8
10.4%
L5
 
6.5%
b5
 
6.5%
t4
 
5.2%
f4
 
5.2%
n3
 
3.9%
Other values (6)8
10.4%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII79
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r13
16.5%
a10
12.7%
s9
11.4%
o8
10.1%
e8
10.1%
L5
 
6.3%
b5
 
6.3%
t4
 
5.1%
f4
 
5.1%
n3
 
3.8%
Other values (7)10
12.7%

CNT_FAM_MEMBERS
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size728.0 B
2.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.05
50.0%
1.04
40.0%
3.01
 
10.0%

Length

2021-09-09T20:00:07.767147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.806025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.05
50.0%
1.04
40.0%
3.01
 
10.0%

Most occurring characters

ValueCountFrequency (%)
.10
33.3%
010
33.3%
25
16.7%
14
 
13.3%
31
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
66.7%
Other Punctuation10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
50.0%
25
25.0%
14
 
20.0%
31
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.10
33.3%
010
33.3%
25
16.7%
14
 
13.3%
31
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.10
33.3%
010
33.3%
25
16.7%
14
 
13.3%
31
 
3.3%

REGION_RATING_CLIENT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
2
1
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Length

2021-09-09T20:00:07.922690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:07.961244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
28
80.0%
31
 
10.0%
11
 
10.0%

Most occurring characters

ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

REGION_RATING_CLIENT_W_CITY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
2
1
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Length

2021-09-09T20:00:08.077418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:08.116031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
28
80.0%
31
 
10.0%
11
 
10.0%

Most occurring characters

ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
80.0%
11
 
10.0%
31
 
10.0%

WEEKDAY_APPR_PROCESS_START
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size774.0 B
WEDNESDAY
MONDAY
THURSDAY
SUNDAY

Length

Max length9
Median length8
Mean length7.6
Min length6

Characters and Unicode

Total characters76
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st rowWEDNESDAY
2nd rowMONDAY
3rd rowMONDAY
4th rowWEDNESDAY
5th rowTHURSDAY

Common Values

ValueCountFrequency (%)
WEDNESDAY4
40.0%
MONDAY3
30.0%
THURSDAY2
20.0%
SUNDAY1
 
10.0%

Length

2021-09-09T20:00:08.230992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:08.275976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
wednesday4
40.0%
monday3
30.0%
thursday2
20.0%
sunday1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
D14
18.4%
A10
13.2%
Y10
13.2%
E8
10.5%
N8
10.5%
S7
9.2%
W4
 
5.3%
M3
 
3.9%
O3
 
3.9%
U3
 
3.9%
Other values (3)6
7.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter76
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D14
18.4%
A10
13.2%
Y10
13.2%
E8
10.5%
N8
10.5%
S7
9.2%
W4
 
5.3%
M3
 
3.9%
O3
 
3.9%
U3
 
3.9%
Other values (3)6
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin76
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D14
18.4%
A10
13.2%
Y10
13.2%
E8
10.5%
N8
10.5%
S7
9.2%
W4
 
5.3%
M3
 
3.9%
O3
 
3.9%
U3
 
3.9%
Other values (3)6
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII76
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D14
18.4%
A10
13.2%
Y10
13.2%
E8
10.5%
N8
10.5%
S7
9.2%
W4
 
5.3%
M3
 
3.9%
O3
 
3.9%
U3
 
3.9%
Other values (3)6
7.9%

HOUR_APPR_PROCESS_START
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.8
Minimum8
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:08.323461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.45
Q110.25
median12.5
Q316
95-th percentile16.55
Maximum17
Range9
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.359894178
Coefficient of variation (CV)0.2624917327
Kurtosis-1.85662416
Mean12.8
Median Absolute Deviation (MAD)3.5
Skewness-0.09139770418
Sum128
Variance11.28888889
MonotonicityNot monotonic
2021-09-09T20:00:08.377277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
163
30.0%
112
20.0%
141
 
10.0%
101
 
10.0%
91
 
10.0%
81
 
10.0%
171
 
10.0%
ValueCountFrequency (%)
81
 
10.0%
91
 
10.0%
101
 
10.0%
112
20.0%
141
 
10.0%
163
30.0%
171
 
10.0%
ValueCountFrequency (%)
171
 
10.0%
163
30.0%
141
 
10.0%
112
20.0%
101
 
10.0%
91
 
10.0%
81
 
10.0%

REG_REGION_NOT_LIVE_REGION
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:08.501582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:08.539254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

REG_REGION_NOT_WORK_REGION
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:08.634338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:08.671912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

LIVE_REGION_NOT_WORK_REGION
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:08.767112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:08.804722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

REG_CITY_NOT_LIVE_CITY
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:08.900004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:08.937414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

REG_CITY_NOT_WORK_CITY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Length

2021-09-09T20:00:09.037322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:09.075401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring characters

ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

LIVE_CITY_NOT_WORK_CITY
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Length

2021-09-09T20:00:09.178451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:09.216514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring characters

ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

ORGANIZATION_TYPE
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
Business Entity Type 3
Other
School
XNA
Religion
Other values (2)

Length

Max length22
Median length9
Mean length11.4
Min length3

Characters and Unicode

Total characters114
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)50.0%

Sample

1st rowBusiness Entity Type 3
2nd rowSchool
3rd rowGovernment
4th rowBusiness Entity Type 3
5th rowReligion

Common Values

ValueCountFrequency (%)
Business Entity Type 33
30.0%
Other2
20.0%
School1
 
10.0%
XNA1
 
10.0%
Religion1
 
10.0%
Electricity1
 
10.0%
Government1
 
10.0%

Length

2021-09-09T20:00:09.337773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:09.390045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
entity3
15.8%
type3
15.8%
33
15.8%
business3
15.8%
other2
10.5%
electricity1
 
5.3%
school1
 
5.3%
government1
 
5.3%
religion1
 
5.3%
xna1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
e12
 
10.5%
t11
 
9.6%
i10
 
8.8%
s9
 
7.9%
n9
 
7.9%
9
 
7.9%
y7
 
6.1%
E4
 
3.5%
o4
 
3.5%
r4
 
3.5%
Other values (18)35
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter84
73.7%
Uppercase Letter18
 
15.8%
Space Separator9
 
7.9%
Decimal Number3
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e12
14.3%
t11
13.1%
i10
11.9%
s9
10.7%
n9
10.7%
y7
8.3%
o4
 
4.8%
r4
 
4.8%
u3
 
3.6%
p3
 
3.6%
Other values (6)12
14.3%
Uppercase Letter
ValueCountFrequency (%)
E4
22.2%
B3
16.7%
T3
16.7%
O2
11.1%
S1
 
5.6%
G1
 
5.6%
R1
 
5.6%
X1
 
5.6%
N1
 
5.6%
A1
 
5.6%
Space Separator
ValueCountFrequency (%)
9
100.0%
Decimal Number
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin102
89.5%
Common12
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e12
11.8%
t11
 
10.8%
i10
 
9.8%
s9
 
8.8%
n9
 
8.8%
y7
 
6.9%
E4
 
3.9%
o4
 
3.9%
r4
 
3.9%
B3
 
2.9%
Other values (16)29
28.4%
Common
ValueCountFrequency (%)
9
75.0%
33
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e12
 
10.5%
t11
 
9.6%
i10
 
8.8%
s9
 
7.9%
n9
 
7.9%
9
 
7.9%
y7
 
6.1%
E4
 
3.5%
o4
 
3.5%
r4
 
3.5%
Other values (18)35
30.7%

EXT_SOURCE_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing6
Missing (%)60.0%
Memory size669.0 B
0.08303696739132256
0.5873340468730377
0.7747614130547695
0.3112673113812225

Length

Max length19
Median length18
Mean length18.25
Min length18

Characters and Unicode

Total characters73
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row0.08303696739132256
2nd row0.3112673113812225
3rd row0.7747614130547695
4th row0.5873340468730377

Common Values

ValueCountFrequency (%)
0.083036967391322561
 
10.0%
0.58733404687303771
 
10.0%
0.77476141305476951
 
10.0%
0.31126731138122251
 
10.0%
(Missing)6
60.0%

Length

2021-09-09T20:00:09.522250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:09.566102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.31126731138122251
25.0%
0.77476141305476951
25.0%
0.58733404687303771
25.0%
0.083036967391322561
25.0%

Most occurring characters

ValueCountFrequency (%)
312
16.4%
710
13.7%
09
12.3%
18
11.0%
67
9.6%
26
8.2%
55
6.8%
45
6.8%
.4
 
5.5%
84
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number69
94.5%
Other Punctuation4
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
312
17.4%
710
14.5%
09
13.0%
18
11.6%
67
10.1%
26
8.7%
55
7.2%
45
7.2%
84
 
5.8%
93
 
4.3%
Other Punctuation
ValueCountFrequency (%)
.4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common73
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
312
16.4%
710
13.7%
09
12.3%
18
11.0%
67
9.6%
26
8.2%
55
6.8%
45
6.8%
.4
 
5.5%
84
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII73
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312
16.4%
710
13.7%
09
12.3%
18
11.0%
67
9.6%
26
8.2%
55
6.8%
45
6.8%
.4
 
5.5%
84
 
5.5%

EXT_SOURCE_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5159181216
Minimum0.205747288
Maximum0.7466436295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:09.614914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.205747288
5-th percentile0.2314878751
Q10.3306098982
median0.5890789293
Q30.6983198874
95-th percentile0.7364539295
Maximum0.7466436295
Range0.5408963415
Interquartile range (IQR)0.3677099892

Descriptive statistics

Standard deviation0.2083116855
Coefficient of variation (CV)0.4037688865
Kurtosis-1.772926701
Mean0.5159181216
Median Absolute Deviation (MAD)0.1462428113
Skewness-0.379016375
Sum5.159181216
Variance0.04339375832
MonotonicityNot monotonic
2021-09-09T20:00:09.675065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.2057472881
10.0%
0.72399985171
10.0%
0.3227382871
10.0%
0.71427928641
10.0%
0.3542247321
10.0%
0.65044169041
10.0%
0.26294859271
10.0%
0.74664362951
10.0%
0.55591208341
10.0%
0.62224577531
10.0%
ValueCountFrequency (%)
0.2057472881
10.0%
0.26294859271
10.0%
0.3227382871
10.0%
0.3542247321
10.0%
0.55591208341
10.0%
0.62224577531
10.0%
0.65044169041
10.0%
0.71427928641
10.0%
0.72399985171
10.0%
0.74664362951
10.0%
ValueCountFrequency (%)
0.74664362951
10.0%
0.72399985171
10.0%
0.71427928641
10.0%
0.65044169041
10.0%
0.62224577531
10.0%
0.55591208341
10.0%
0.3542247321
10.0%
0.3227382871
10.0%
0.26294859271
10.0%
0.2057472881
10.0%

EXT_SOURCE_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)100.0%
Missing4
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean0.5457678447
Minimum0.1393757801
Maximum0.7517237148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2021-09-09T20:00:09.729888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1393757801
5-th percentile0.2275468585
Q10.504208683
median0.5809403943
Q30.7024816025
95-th percentile0.7461844588
Maximum0.7517237148
Range0.6123479347
Interquartile range (IQR)0.1982729195

Descriptive statistics

Standard deviation0.2235884612
Coefficient of variation (CV)0.4096768679
Kurtosis2.224809771
Mean0.5457678447
Median Absolute Deviation (MAD)0.1187532984
Skewness-1.392900926
Sum3.274607068
Variance0.04999179998
MonotonicityNot monotonic
2021-09-09T20:00:09.782043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.72956669071
 
10.0%
0.13937578011
 
10.0%
0.54065445041
 
10.0%
0.75172371481
 
10.0%
0.62122633811
 
10.0%
0.49206009391
 
10.0%
(Missing)4
40.0%
ValueCountFrequency (%)
0.13937578011
10.0%
0.49206009391
10.0%
0.54065445041
10.0%
0.62122633811
10.0%
0.72956669071
10.0%
0.75172371481
10.0%
ValueCountFrequency (%)
0.75172371481
10.0%
0.72956669071
10.0%
0.62122633811
10.0%
0.54065445041
10.0%
0.49206009391
10.0%
0.13937578011
10.0%

APARTMENTS_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0247
0.0959

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0247
2nd row0.0959

Common Values

ValueCountFrequency (%)
0.02471
 
10.0%
0.09591
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:09.911098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:09.952973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09591
50.0%
0.02471
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
21
 
8.3%
41
 
8.3%
71
 
8.3%
51
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
92
20.0%
21
 
10.0%
41
 
10.0%
71
 
10.0%
51
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
21
 
8.3%
41
 
8.3%
71
 
8.3%
51
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
21
 
8.3%
41
 
8.3%
71
 
8.3%
51
 
8.3%

BASEMENTAREA_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0369
0.0529

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0369
2nd row0.0529

Common Values

ValueCountFrequency (%)
0.03691
 
10.0%
0.05291
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.061839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:10.103712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05291
50.0%
0.03691
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
31
 
8.3%
61
 
8.3%
51
 
8.3%
21
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
92
20.0%
31
 
10.0%
61
 
10.0%
51
 
10.0%
21
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
31
 
8.3%
61
 
8.3%
51
 
8.3%
21
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
31
 
8.3%
61
 
8.3%
51
 
8.3%
21
 
8.3%

YEARS_BEGINEXPLUATATION_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.9851
0.9722

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.9722
2nd row0.9851

Common Values

ValueCountFrequency (%)
0.98511
 
10.0%
0.97221
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.212450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:10.254249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.97221
50.0%
0.98511
50.0%

Most occurring characters

ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
20.0%
92
20.0%
22
20.0%
71
10.0%
81
10.0%
51
10.0%
11
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

YEARS_BUILD_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size586.0 B
0.7959999999999999
0.6192

Length

Max length18
Median length12
Mean length12
Min length6

Characters and Unicode

Total characters24
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.6192
2nd row0.7959999999999999

Common Values

ValueCountFrequency (%)
0.79599999999999991
 
10.0%
0.61921
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.362438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:10.404139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.61921
50.0%
0.79599999999999991
50.0%

Most occurring characters

ValueCountFrequency (%)
915
62.5%
02
 
8.3%
.2
 
8.3%
61
 
4.2%
11
 
4.2%
21
 
4.2%
71
 
4.2%
51
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22
91.7%
Other Punctuation2
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
915
68.2%
02
 
9.1%
61
 
4.5%
11
 
4.5%
21
 
4.5%
71
 
4.5%
51
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
915
62.5%
02
 
8.3%
.2
 
8.3%
61
 
4.2%
11
 
4.2%
21
 
4.2%
71
 
4.2%
51
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
915
62.5%
02
 
8.3%
.2
 
8.3%
61
 
4.2%
11
 
4.2%
21
 
4.2%
71
 
4.2%
51
 
4.2%

COMMONAREA_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0143
0.0605

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0143
2nd row0.0605

Common Values

ValueCountFrequency (%)
0.01431
 
10.0%
0.06051
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.513684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:10.555529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06051
50.0%
0.01431
50.0%

Most occurring characters

ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
11
 
8.3%
41
 
8.3%
31
 
8.3%
61
 
8.3%
51
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
50.0%
11
 
10.0%
41
 
10.0%
31
 
10.0%
61
 
10.0%
51
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
11
 
8.3%
41
 
8.3%
31
 
8.3%
61
 
8.3%
51
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
11
 
8.3%
41
 
8.3%
31
 
8.3%
61
 
8.3%
51
 
8.3%

ELEVATORS_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size569.0 B
0.08
0.0

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters7
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.08

Common Values

ValueCountFrequency (%)
0.081
 
10.0%
0.01
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.662281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:10.703061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01
50.0%
0.081
50.0%

Most occurring characters

ValueCountFrequency (%)
04
57.1%
.2
28.6%
81
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5
71.4%
Other Punctuation2
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
80.0%
81
 
20.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
57.1%
.2
28.6%
81
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
57.1%
.2
28.6%
81
 
14.3%

ENTRANCES_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.069
0.0345

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.069
2nd row0.0345

Common Values

ValueCountFrequency (%)
0.0691
 
10.0%
0.03451
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.810459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:10.851774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03451
50.0%
0.0691
50.0%

Most occurring characters

ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
44.4%
61
 
11.1%
91
 
11.1%
31
 
11.1%
41
 
11.1%
51
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

FLOORSMAX_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0833
0.2917

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0833
2nd row0.2917

Common Values

ValueCountFrequency (%)
0.08331
 
10.0%
0.29171
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:10.960268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.001914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.29171
50.0%
0.08331
50.0%

Most occurring characters

ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
30.0%
32
20.0%
81
 
10.0%
21
 
10.0%
91
 
10.0%
11
 
10.0%
71
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

FLOORSMIN_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.3333
0.125

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.125
2nd row0.3333

Common Values

ValueCountFrequency (%)
0.33331
 
10.0%
0.1251
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:11.109202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.149931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1251
50.0%
0.33331
50.0%

Most occurring characters

ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
34
44.4%
02
22.2%
11
 
11.1%
21
 
11.1%
51
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

LANDAREA_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size588.0 B
0.0369
0.013000000000000001

Length

Max length20
Median length13
Mean length13
Min length6

Characters and Unicode

Total characters26
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0369
2nd row0.013000000000000001

Common Values

ValueCountFrequency (%)
0.03691
 
10.0%
0.0130000000000000011
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:11.259822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.302793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0130000000000000011
50.0%
0.03691
50.0%

Most occurring characters

ValueCountFrequency (%)
018
69.2%
.2
 
7.7%
32
 
7.7%
12
 
7.7%
61
 
3.8%
91
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24
92.3%
Other Punctuation2
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018
75.0%
32
 
8.3%
12
 
8.3%
61
 
4.2%
91
 
4.2%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018
69.2%
.2
 
7.7%
32
 
7.7%
12
 
7.7%
61
 
3.8%
91
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018
69.2%
.2
 
7.7%
32
 
7.7%
12
 
7.7%
61
 
3.8%
91
 
3.8%

LIVINGAPARTMENTS_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0202
0.0773

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0202
2nd row0.0773

Common Values

ValueCountFrequency (%)
0.02021
 
10.0%
0.07731
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:11.413649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.455637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07731
50.0%
0.02021
50.0%

Most occurring characters

ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
22
 
16.7%
72
 
16.7%
31
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
50.0%
22
 
20.0%
72
 
20.0%
31
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
22
 
16.7%
72
 
16.7%
31
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
22
 
16.7%
72
 
16.7%
31
 
8.3%

LIVINGAREA_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.0549
0.019

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.019
2nd row0.0549

Common Values

ValueCountFrequency (%)
0.05491
 
10.0%
0.0191
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:11.562663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.603461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0191
50.0%
0.05491
50.0%

Most occurring characters

ValueCountFrequency (%)
04
36.4%
.2
18.2%
92
18.2%
11
 
9.1%
51
 
9.1%
41
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
44.4%
92
22.2%
11
 
11.1%
51
 
11.1%
41
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
36.4%
.2
18.2%
92
18.2%
11
 
9.1%
51
 
9.1%
41
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
36.4%
.2
18.2%
92
18.2%
11
 
9.1%
51
 
9.1%
41
 
9.1%

NONLIVINGAPARTMENTS_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size571.0 B
0.0039
0.0

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.0039

Common Values

ValueCountFrequency (%)
0.00391
 
10.0%
0.01
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:11.711894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.754271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01
50.0%
0.00391
50.0%

Most occurring characters

ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
31
 
11.1%
91
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7
77.8%
Other Punctuation2
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
71.4%
31
 
14.3%
91
 
14.3%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
31
 
11.1%
91
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
31
 
11.1%
91
 
11.1%

NONLIVINGAREA_AVG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size571.0 B
0.0098
0.0

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.0098

Common Values

ValueCountFrequency (%)
0.00981
 
10.0%
0.01
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:11.864115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:11.906550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01
50.0%
0.00981
50.0%

Most occurring characters

ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
91
 
11.1%
81
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7
77.8%
Other Punctuation2
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
71.4%
91
 
14.3%
81
 
14.3%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
91
 
11.1%
81
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
91
 
11.1%
81
 
11.1%

APARTMENTS_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0252
0.0924

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0252
2nd row0.0924

Common Values

ValueCountFrequency (%)
0.02521
 
10.0%
0.09241
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.015569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.057908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09241
50.0%
0.02521
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
23
25.0%
.2
16.7%
51
 
8.3%
91
 
8.3%
41
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
23
30.0%
51
 
10.0%
91
 
10.0%
41
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
23
25.0%
.2
16.7%
51
 
8.3%
91
 
8.3%
41
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
23
25.0%
.2
16.7%
51
 
8.3%
91
 
8.3%
41
 
8.3%

BASEMENTAREA_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0538
0.0383

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0383
2nd row0.0538

Common Values

ValueCountFrequency (%)
0.05381
 
10.0%
0.03831
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.166358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.208102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03831
50.0%
0.05381
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
33
25.0%
.2
16.7%
82
16.7%
51
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
33
30.0%
82
20.0%
51
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
33
25.0%
.2
16.7%
82
16.7%
51
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
33
25.0%
.2
16.7%
82
16.7%
51
 
8.3%

YEARS_BEGINEXPLUATATION_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.9851
0.9722

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.9722
2nd row0.9851

Common Values

ValueCountFrequency (%)
0.98511
 
10.0%
0.97221
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.316954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.359154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.97221
50.0%
0.98511
50.0%

Most occurring characters

ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
20.0%
92
20.0%
22
20.0%
71
10.0%
81
10.0%
51
10.0%
11
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

YEARS_BUILD_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.6341
0.804

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.6341
2nd row0.804

Common Values

ValueCountFrequency (%)
0.63411
 
10.0%
0.8041
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.466556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.508660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.8041
50.0%
0.63411
50.0%

Most occurring characters

ValueCountFrequency (%)
03
27.3%
.2
18.2%
42
18.2%
61
 
9.1%
31
 
9.1%
11
 
9.1%
81
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
33.3%
42
22.2%
61
 
11.1%
31
 
11.1%
11
 
11.1%
81
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03
27.3%
.2
18.2%
42
18.2%
61
 
9.1%
31
 
9.1%
11
 
9.1%
81
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03
27.3%
.2
18.2%
42
18.2%
61
 
9.1%
31
 
9.1%
11
 
9.1%
81
 
9.1%

COMMONAREA_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0144
0.0497

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0144
2nd row0.0497

Common Values

ValueCountFrequency (%)
0.01441
 
10.0%
0.04971
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.617097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.658843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04971
50.0%
0.01441
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
43
25.0%
.2
16.7%
11
 
8.3%
91
 
8.3%
71
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
43
30.0%
11
 
10.0%
91
 
10.0%
71
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
43
25.0%
.2
16.7%
11
 
8.3%
91
 
8.3%
71
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
43
25.0%
.2
16.7%
11
 
8.3%
91
 
8.3%
71
 
8.3%

ELEVATORS_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size571.0 B
0.0
0.0806

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.0806

Common Values

ValueCountFrequency (%)
0.01
 
10.0%
0.08061
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.767129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.809885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08061
50.0%
0.01
50.0%

Most occurring characters

ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
81
 
11.1%
61
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7
77.8%
Other Punctuation2
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
71.4%
81
 
14.3%
61
 
14.3%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
81
 
11.1%
61
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
81
 
11.1%
61
 
11.1%

ENTRANCES_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.069
0.0345

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.069
2nd row0.0345

Common Values

ValueCountFrequency (%)
0.0691
 
10.0%
0.03451
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:12.917121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:12.958004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03451
50.0%
0.0691
50.0%

Most occurring characters

ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
44.4%
61
 
11.1%
91
 
11.1%
31
 
11.1%
41
 
11.1%
51
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

FLOORSMAX_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0833
0.2917

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0833
2nd row0.2917

Common Values

ValueCountFrequency (%)
0.08331
 
10.0%
0.29171
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:13.067069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:13.109157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.29171
50.0%
0.08331
50.0%

Most occurring characters

ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
30.0%
32
20.0%
81
 
10.0%
21
 
10.0%
91
 
10.0%
11
 
10.0%
71
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

FLOORSMIN_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.3333
0.125

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.125
2nd row0.3333

Common Values

ValueCountFrequency (%)
0.33331
 
10.0%
0.1251
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:13.216858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:13.257988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1251
50.0%
0.33331
50.0%

Most occurring characters

ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
34
44.4%
02
22.2%
11
 
11.1%
21
 
11.1%
51
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

LANDAREA_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0377
0.0128

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0377
2nd row0.0128

Common Values

ValueCountFrequency (%)
0.03771
 
10.0%
0.01281
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:13.406895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:13.450065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01281
50.0%
0.03771
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
72
16.7%
31
 
8.3%
11
 
8.3%
21
 
8.3%
81
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
72
20.0%
31
 
10.0%
11
 
10.0%
21
 
10.0%
81
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
72
16.7%
31
 
8.3%
11
 
8.3%
21
 
8.3%
81
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
72
16.7%
31
 
8.3%
11
 
8.3%
21
 
8.3%
81
 
8.3%

LIVINGAPARTMENTS_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size587.0 B
0.079
0.022000000000000002

Length

Max length20
Median length12.5
Mean length12.5
Min length5

Characters and Unicode

Total characters25
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.022000000000000002
2nd row0.079

Common Values

ValueCountFrequency (%)
0.0791
 
10.0%
0.0220000000000000021
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:13.564562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:13.608751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0220000000000000021
50.0%
0.0791
50.0%

Most occurring characters

ValueCountFrequency (%)
018
72.0%
23
 
12.0%
.2
 
8.0%
71
 
4.0%
91
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23
92.0%
Other Punctuation2
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018
78.3%
23
 
13.0%
71
 
4.3%
91
 
4.3%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018
72.0%
23
 
12.0%
.2
 
8.0%
71
 
4.0%
91
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018
72.0%
23
 
12.0%
.2
 
8.0%
71
 
4.0%
91
 
4.0%

LIVINGAREA_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0198
0.0554

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0198
2nd row0.0554

Common Values

ValueCountFrequency (%)
0.01981
 
10.0%
0.05541
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:13.718234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:13.760164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05541
50.0%
0.01981
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
52
16.7%
11
 
8.3%
91
 
8.3%
81
 
8.3%
41
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
52
20.0%
11
 
10.0%
91
 
10.0%
81
 
10.0%
41
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
52
16.7%
11
 
8.3%
91
 
8.3%
81
 
8.3%
41
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
52
16.7%
11
 
8.3%
91
 
8.3%
81
 
8.3%
41
 
8.3%

NONLIVINGAPARTMENTS_MODE
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing8
Missing (%)80.0%
Memory size568.0 B
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0

Common Values

ValueCountFrequency (%)
0.02
 
20.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:13.862078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:13.903338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02
100.0%

Most occurring characters

ValueCountFrequency (%)
04
66.7%
.2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4
66.7%
Other Punctuation2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
100.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
66.7%
.2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
66.7%
.2
33.3%

NONLIVINGAREA_MODE
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing8
Missing (%)80.0%
Memory size568.0 B
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0

Common Values

ValueCountFrequency (%)
0.02
 
20.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.001095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.042367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02
100.0%

Most occurring characters

ValueCountFrequency (%)
04
66.7%
.2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4
66.7%
Other Punctuation2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
100.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
66.7%
.2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
66.7%
.2
33.3%

APARTMENTS_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.025
0.0968

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.025
2nd row0.0968

Common Values

ValueCountFrequency (%)
0.0251
 
10.0%
0.09681
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.150037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.192476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09681
50.0%
0.0251
50.0%

Most occurring characters

ValueCountFrequency (%)
04
36.4%
.2
18.2%
21
 
9.1%
51
 
9.1%
91
 
9.1%
61
 
9.1%
81
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
44.4%
21
 
11.1%
51
 
11.1%
91
 
11.1%
61
 
11.1%
81
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
36.4%
.2
18.2%
21
 
9.1%
51
 
9.1%
91
 
9.1%
61
 
9.1%
81
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
36.4%
.2
18.2%
21
 
9.1%
51
 
9.1%
91
 
9.1%
61
 
9.1%
81
 
9.1%

BASEMENTAREA_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0369
0.0529

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0369
2nd row0.0529

Common Values

ValueCountFrequency (%)
0.03691
 
10.0%
0.05291
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.301734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.344631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05291
50.0%
0.03691
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
31
 
8.3%
61
 
8.3%
51
 
8.3%
21
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
92
20.0%
31
 
10.0%
61
 
10.0%
51
 
10.0%
21
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
31
 
8.3%
61
 
8.3%
51
 
8.3%
21
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
92
16.7%
31
 
8.3%
61
 
8.3%
51
 
8.3%
21
 
8.3%

YEARS_BEGINEXPLUATATION_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.9851
0.9722

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.9722
2nd row0.9851

Common Values

ValueCountFrequency (%)
0.98511
 
10.0%
0.97221
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.454684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.497329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.97221
50.0%
0.98511
50.0%

Most occurring characters

ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
20.0%
92
20.0%
22
20.0%
71
10.0%
81
10.0%
51
10.0%
11
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02
16.7%
.2
16.7%
92
16.7%
22
16.7%
71
8.3%
81
8.3%
51
8.3%
11
8.3%

YEARS_BUILD_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.6243
0.7987

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.6243
2nd row0.7987

Common Values

ValueCountFrequency (%)
0.62431
 
10.0%
0.79871
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.649593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.692933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.79871
50.0%
0.62431
50.0%

Most occurring characters

ValueCountFrequency (%)
02
16.7%
.2
16.7%
72
16.7%
61
8.3%
21
8.3%
41
8.3%
31
8.3%
91
8.3%
81
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
20.0%
72
20.0%
61
10.0%
21
10.0%
41
10.0%
31
10.0%
91
10.0%
81
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02
16.7%
.2
16.7%
72
16.7%
61
8.3%
21
8.3%
41
8.3%
31
8.3%
91
8.3%
81
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02
16.7%
.2
16.7%
72
16.7%
61
8.3%
21
8.3%
41
8.3%
31
8.3%
91
8.3%
81
8.3%

COMMONAREA_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0144
0.0608

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0144
2nd row0.0608

Common Values

ValueCountFrequency (%)
0.01441
 
10.0%
0.06081
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.805075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.848523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06081
50.0%
0.01441
50.0%

Most occurring characters

ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
42
 
16.7%
11
 
8.3%
61
 
8.3%
81
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
50.0%
42
 
20.0%
11
 
10.0%
61
 
10.0%
81
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
42
 
16.7%
11
 
8.3%
61
 
8.3%
81
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
42
 
16.7%
11
 
8.3%
61
 
8.3%
81
 
8.3%

ELEVATORS_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size569.0 B
0.08
0.0

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters7
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.08

Common Values

ValueCountFrequency (%)
0.081
 
10.0%
0.01
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:14.955801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:14.996598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01
50.0%
0.081
50.0%

Most occurring characters

ValueCountFrequency (%)
04
57.1%
.2
28.6%
81
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5
71.4%
Other Punctuation2
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
80.0%
81
 
20.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
57.1%
.2
28.6%
81
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
57.1%
.2
28.6%
81
 
14.3%

ENTRANCES_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.069
0.0345

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.069
2nd row0.0345

Common Values

ValueCountFrequency (%)
0.0691
 
10.0%
0.03451
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:15.106067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:15.158788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03451
50.0%
0.0691
50.0%

Most occurring characters

ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
44.4%
61
 
11.1%
91
 
11.1%
31
 
11.1%
41
 
11.1%
51
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
36.4%
.2
18.2%
61
 
9.1%
91
 
9.1%
31
 
9.1%
41
 
9.1%
51
 
9.1%

FLOORSMAX_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0833
0.2917

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0833
2nd row0.2917

Common Values

ValueCountFrequency (%)
0.08331
 
10.0%
0.29171
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:15.275932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:15.318373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.29171
50.0%
0.08331
50.0%

Most occurring characters

ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
30.0%
32
20.0%
81
 
10.0%
21
 
10.0%
91
 
10.0%
11
 
10.0%
71
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03
25.0%
.2
16.7%
32
16.7%
81
 
8.3%
21
 
8.3%
91
 
8.3%
11
 
8.3%
71
 
8.3%

FLOORSMIN_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size573.0 B
0.3333
0.125

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters11
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.125
2nd row0.3333

Common Values

ValueCountFrequency (%)
0.33331
 
10.0%
0.1251
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:15.426709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:15.467844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1251
50.0%
0.33331
50.0%

Most occurring characters

ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9
81.8%
Other Punctuation2
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
34
44.4%
02
22.2%
11
 
11.1%
21
 
11.1%
51
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
36.4%
02
18.2%
.2
18.2%
11
 
9.1%
21
 
9.1%
51
 
9.1%

LANDAREA_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0132
0.0375

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0375
2nd row0.0132

Common Values

ValueCountFrequency (%)
0.01321
 
10.0%
0.03751
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:15.577824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:15.619952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03751
50.0%
0.01321
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
32
16.7%
71
 
8.3%
51
 
8.3%
11
 
8.3%
21
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
32
20.0%
71
 
10.0%
51
 
10.0%
11
 
10.0%
21
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
32
16.7%
71
 
8.3%
51
 
8.3%
11
 
8.3%
21
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
32
16.7%
71
 
8.3%
51
 
8.3%
11
 
8.3%
21
 
8.3%

LIVINGAPARTMENTS_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0787
0.0205

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0205
2nd row0.0787

Common Values

ValueCountFrequency (%)
0.07871
 
10.0%
0.02051
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:15.728917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:15.770912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02051
50.0%
0.07871
50.0%

Most occurring characters

ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
72
 
16.7%
21
 
8.3%
51
 
8.3%
81
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
50.0%
72
 
20.0%
21
 
10.0%
51
 
10.0%
81
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
72
 
16.7%
21
 
8.3%
51
 
8.3%
81
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
41.7%
.2
 
16.7%
72
 
16.7%
21
 
8.3%
51
 
8.3%
81
 
8.3%

LIVINGAREA_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0193
0.0558

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0193
2nd row0.0558

Common Values

ValueCountFrequency (%)
0.01931
 
10.0%
0.05581
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:15.879479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:15.921179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.05581
50.0%
0.01931
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
52
16.7%
11
 
8.3%
91
 
8.3%
31
 
8.3%
81
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
52
20.0%
11
 
10.0%
91
 
10.0%
31
 
10.0%
81
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
52
16.7%
11
 
8.3%
91
 
8.3%
31
 
8.3%
81
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
52
16.7%
11
 
8.3%
91
 
8.3%
31
 
8.3%
81
 
8.3%

NONLIVINGAPARTMENTS_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size571.0 B
0.0039
0.0

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.0039

Common Values

ValueCountFrequency (%)
0.00391
 
10.0%
0.01
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:16.029840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.072321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01
50.0%
0.00391
50.0%

Most occurring characters

ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
31
 
11.1%
91
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7
77.8%
Other Punctuation2
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
71.4%
31
 
14.3%
91
 
14.3%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
31
 
11.1%
91
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05
55.6%
.2
 
22.2%
31
 
11.1%
91
 
11.1%

NONLIVINGAREA_MEDI
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size569.0 B
0.01
0.0

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters7
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.01

Common Values

ValueCountFrequency (%)
0.011
 
10.0%
0.01
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:16.179503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.221325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01
50.0%
0.011
50.0%

Most occurring characters

ValueCountFrequency (%)
04
57.1%
.2
28.6%
11
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5
71.4%
Other Punctuation2
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
80.0%
11
 
20.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
57.1%
.2
28.6%
11
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
57.1%
.2
28.6%
11
 
14.3%

FONDKAPREMONT_MODE
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing8
Missing (%)80.0%
Memory size530.0 B
reg oper account

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters32
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreg oper account
2nd rowreg oper account

Common Values

ValueCountFrequency (%)
reg oper account2
 
20.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:16.330151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.371980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
account2
33.3%
oper2
33.3%
reg2
33.3%

Most occurring characters

ValueCountFrequency (%)
r4
12.5%
e4
12.5%
4
12.5%
o4
12.5%
c4
12.5%
g2
6.2%
p2
6.2%
a2
6.2%
u2
6.2%
n2
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28
87.5%
Space Separator4
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r4
14.3%
e4
14.3%
o4
14.3%
c4
14.3%
g2
7.1%
p2
7.1%
a2
7.1%
u2
7.1%
n2
7.1%
t2
7.1%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28
87.5%
Common4
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r4
14.3%
e4
14.3%
o4
14.3%
c4
14.3%
g2
7.1%
p2
7.1%
a2
7.1%
u2
7.1%
n2
7.1%
t2
7.1%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r4
12.5%
e4
12.5%
4
12.5%
o4
12.5%
c4
12.5%
g2
6.2%
p2
6.2%
a2
6.2%
u2
6.2%
n2
6.2%

HOUSETYPE_MODE
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing8
Missing (%)80.0%
Memory size526.0 B
block of flats

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters28
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblock of flats
2nd rowblock of flats

Common Values

ValueCountFrequency (%)
block of flats2
 
20.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:16.470930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.512772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
of2
33.3%
flats2
33.3%
block2
33.3%

Most occurring characters

ValueCountFrequency (%)
l4
14.3%
o4
14.3%
4
14.3%
f4
14.3%
b2
7.1%
c2
7.1%
k2
7.1%
a2
7.1%
t2
7.1%
s2
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24
85.7%
Space Separator4
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l4
16.7%
o4
16.7%
f4
16.7%
b2
8.3%
c2
8.3%
k2
8.3%
a2
8.3%
t2
8.3%
s2
8.3%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24
85.7%
Common4
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l4
16.7%
o4
16.7%
f4
16.7%
b2
8.3%
c2
8.3%
k2
8.3%
a2
8.3%
t2
8.3%
s2
8.3%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l4
14.3%
o4
14.3%
4
14.3%
f4
14.3%
b2
7.1%
c2
7.1%
k2
7.1%
a2
7.1%
t2
7.1%
s2
7.1%

TOTALAREA_MODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size574.0 B
0.0149
0.0714

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0149
2nd row0.0714

Common Values

ValueCountFrequency (%)
0.01491
 
10.0%
0.07141
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:16.618433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.660875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07141
50.0%
0.01491
50.0%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.2
16.7%
12
16.7%
42
16.7%
91
 
8.3%
71
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
83.3%
Other Punctuation2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
40.0%
12
20.0%
42
20.0%
91
 
10.0%
71
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04
33.3%
.2
16.7%
12
16.7%
42
16.7%
91
 
8.3%
71
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04
33.3%
.2
16.7%
12
16.7%
42
16.7%
91
 
8.3%
71
 
8.3%

WALLSMATERIAL_MODE
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing8
Missing (%)80.0%
Memory size515.0 B
Block
Stone, brick

Length

Max length12
Median length8.5
Mean length8.5
Min length5

Characters and Unicode

Total characters17
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowStone, brick
2nd rowBlock

Common Values

ValueCountFrequency (%)
Block1
 
10.0%
Stone, brick1
 
10.0%
(Missing)8
80.0%

Length

2021-09-09T20:00:16.770648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.814664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
brick1
33.3%
block1
33.3%
stone1
33.3%

Most occurring characters

ValueCountFrequency (%)
o2
11.8%
c2
11.8%
k2
11.8%
S1
 
5.9%
t1
 
5.9%
n1
 
5.9%
e1
 
5.9%
,1
 
5.9%
1
 
5.9%
b1
 
5.9%
Other values (4)4
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13
76.5%
Uppercase Letter2
 
11.8%
Other Punctuation1
 
5.9%
Space Separator1
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o2
15.4%
c2
15.4%
k2
15.4%
t1
7.7%
n1
7.7%
e1
7.7%
b1
7.7%
r1
7.7%
i1
7.7%
l1
7.7%
Uppercase Letter
ValueCountFrequency (%)
S1
50.0%
B1
50.0%
Other Punctuation
ValueCountFrequency (%)
,1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15
88.2%
Common2
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o2
13.3%
c2
13.3%
k2
13.3%
S1
6.7%
t1
6.7%
n1
6.7%
e1
6.7%
b1
6.7%
r1
6.7%
i1
6.7%
Other values (2)2
13.3%
Common
ValueCountFrequency (%)
,1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o2
11.8%
c2
11.8%
k2
11.8%
S1
 
5.9%
t1
 
5.9%
n1
 
5.9%
e1
 
5.9%
,1
 
5.9%
1
 
5.9%
b1
 
5.9%
Other values (4)4
23.5%

EMERGENCYSTATE_MODE
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)50.0%
Missing8
Missing (%)80.0%
Memory size148.0 B
False
(Missing)
ValueCountFrequency (%)
False2
 
20.0%
(Missing)8
80.0%
2021-09-09T20:00:16.838459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

OBS_30_CNT_SOCIAL_CIRCLE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size728.0 B
2.0
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row2.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.04
40.0%
1.03
30.0%
0.03
30.0%

Length

2021-09-09T20:00:16.943337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:16.982310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04
40.0%
0.03
30.0%
1.03
30.0%

Most occurring characters

ValueCountFrequency (%)
013
43.3%
.10
33.3%
24
 
13.3%
13
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
66.7%
Other Punctuation10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013
65.0%
24
 
20.0%
13
 
15.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013
43.3%
.10
33.3%
24
 
13.3%
13
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013
43.3%
.10
33.3%
24
 
13.3%
13
 
10.0%

DEF_30_CNT_SOCIAL_CIRCLE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size728.0 B
0.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09
90.0%
2.01
 
10.0%

Length

2021-09-09T20:00:17.095154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:17.133566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09
90.0%
2.01
 
10.0%

Most occurring characters

ValueCountFrequency (%)
019
63.3%
.10
33.3%
21
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
66.7%
Other Punctuation10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019
95.0%
21
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019
63.3%
.10
33.3%
21
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019
63.3%
.10
33.3%
21
 
3.3%

OBS_60_CNT_SOCIAL_CIRCLE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size728.0 B
2.0
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row2.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.04
40.0%
1.03
30.0%
0.03
30.0%

Length

2021-09-09T20:00:17.243128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:17.282338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2.04
40.0%
0.03
30.0%
1.03
30.0%

Most occurring characters

ValueCountFrequency (%)
013
43.3%
.10
33.3%
24
 
13.3%
13
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
66.7%
Other Punctuation10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013
65.0%
24
 
20.0%
13
 
15.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013
43.3%
.10
33.3%
24
 
13.3%
13
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013
43.3%
.10
33.3%
24
 
13.3%
13
 
10.0%

DEF_60_CNT_SOCIAL_CIRCLE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size728.0 B
0.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09
90.0%
2.01
 
10.0%

Length

2021-09-09T20:00:17.395521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:17.433517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09
90.0%
2.01
 
10.0%

Most occurring characters

ValueCountFrequency (%)
019
63.3%
.10
33.3%
21
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20
66.7%
Other Punctuation10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019
95.0%
21
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019
63.3%
.10
33.3%
21
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019
63.3%
.10
33.3%
21
 
3.3%

DAYS_LAST_PHONE_CHANGE
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE
ZEROS

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1134.1
Minimum-2536
Maximum0
Zeros1
Zeros (%)10.0%
Negative9
Negative (%)90.0%
Memory size208.0 B
2021-09-09T20:00:17.468884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2536
5-th percentile-2147.65
Q1-1455
median-1088
Q3-818.25
95-th percentile-277.65
Maximum0
Range2536
Interquartile range (IQR)636.75

Descriptive statistics

Standard deviation682.546775
Coefficient of variation (CV)-0.6018400273
Kurtosis1.418163117
Mean-1134.1
Median Absolute Deviation (MAD)372
Skewness-0.5816488087
Sum-11341
Variance465870.1
MonotonicityNot monotonic
2021-09-09T20:00:17.532975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-16731
10.0%
01
10.0%
-10701
10.0%
-15621
10.0%
-25361
10.0%
-11061
10.0%
-6171
10.0%
-8151
10.0%
-8281
10.0%
-11341
10.0%
ValueCountFrequency (%)
-25361
10.0%
-16731
10.0%
-15621
10.0%
-11341
10.0%
-11061
10.0%
-10701
10.0%
-8281
10.0%
-8151
10.0%
-6171
10.0%
01
10.0%
ValueCountFrequency (%)
01
10.0%
-6171
10.0%
-8151
10.0%
-8281
10.0%
-10701
10.0%
-11061
10.0%
-11341
10.0%
-15621
10.0%
-16731
10.0%
-25361
10.0%

FLAG_DOCUMENT_2
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:17.651824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:17.689411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
1
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
16
60.0%
04
40.0%

Length

2021-09-09T20:00:17.796658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:17.834735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16
60.0%
04
40.0%

Most occurring characters

ValueCountFrequency (%)
16
60.0%
04
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
16
60.0%
04
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
16
60.0%
04
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
60.0%
04
40.0%

FLAG_DOCUMENT_4
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:17.933144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:17.970530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_5
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.065363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.102826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_6
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.196852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.234078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_7
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.328947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.366586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Length

2021-09-09T20:00:18.466040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.503871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring characters

ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08
80.0%
12
 
20.0%

FLAG_DOCUMENT_9
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.601769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.639257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_10
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.733642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.771020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_11
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.865594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:18.903054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_12
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:18.998284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.035780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_13
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:19.139828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.178032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_14
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Length

2021-09-09T20:00:19.284421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.323111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring characters

ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09
90.0%
11
 
10.0%

FLAG_DOCUMENT_15
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:19.422266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.459890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_16
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:19.555573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.593049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_17
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:19.688918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.726465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_18
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:19.831367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:19.922256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_19
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:20.029074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.068580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_20
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:20.166313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.204738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

FLAG_DOCUMENT_21
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2021-09-09T20:00:20.300104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.337876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

AMT_REQ_CREDIT_BUREAU_HOUR
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)12.5%
Missing2
Missing (%)20.0%
Memory size688.0 B
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08
80.0%
(Missing)2
 
20.0%

Length

2021-09-09T20:00:20.435898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.475805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08
100.0%

Most occurring characters

ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016
100.0%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016
66.7%
.8
33.3%

AMT_REQ_CREDIT_BUREAU_DAY
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)12.5%
Missing2
Missing (%)20.0%
Memory size688.0 B
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08
80.0%
(Missing)2
 
20.0%

Length

2021-09-09T20:00:20.573871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.613792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08
100.0%

Most occurring characters

ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016
100.0%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016
66.7%
.8
33.3%

AMT_REQ_CREDIT_BUREAU_WEEK
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)12.5%
Missing2
Missing (%)20.0%
Memory size688.0 B
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08
80.0%
(Missing)2
 
20.0%

Length

2021-09-09T20:00:20.749373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.802634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08
100.0%

Most occurring characters

ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016
100.0%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016
66.7%
.8
33.3%

AMT_REQ_CREDIT_BUREAU_MON
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)25.0%
Missing2
Missing (%)20.0%
Memory size688.0 B
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07
70.0%
1.01
 
10.0%
(Missing)2
 
20.0%

Length

2021-09-09T20:00:20.933411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:20.979892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07
87.5%
1.01
 
12.5%

Most occurring characters

ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015
93.8%
11
 
6.2%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

AMT_REQ_CREDIT_BUREAU_QRT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)25.0%
Missing2
Missing (%)20.0%
Memory size688.0 B
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.06
60.0%
1.02
 
20.0%
(Missing)2
 
20.0%

Length

2021-09-09T20:00:21.101612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:21.147324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06
75.0%
1.02
 
25.0%

Most occurring characters

ValueCountFrequency (%)
014
58.3%
.8
33.3%
12
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014
87.5%
12
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
014
58.3%
.8
33.3%
12
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014
58.3%
.8
33.3%
12
 
8.3%

AMT_REQ_CREDIT_BUREAU_YEAR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)37.5%
Missing2
Missing (%)20.0%
Memory size688.0 B
0.0
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.04
40.0%
1.03
30.0%
2.01
 
10.0%
(Missing)2
20.0%

Length

2021-09-09T20:00:21.275207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T20:00:21.329249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04
50.0%
1.03
37.5%
2.01
 
12.5%

Most occurring characters

ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012
75.0%
13
 
18.8%
21
 
6.2%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

Interactions

2021-09-09T19:59:47.120940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.186257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.244645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.305784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.370334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.430786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.491725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.550119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.611850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.672106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.733733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.789599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.846667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.901532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:47.959727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.017702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.076185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.137714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.199513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.260378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.321811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.380578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.442505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.503306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.565387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.621875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.679618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.736937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.795415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.857887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.921696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:48.987688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.053647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.118316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.184067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.247063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.313487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.378879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.445891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.507132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.569509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.631782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.695391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.756835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.819747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.885338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:49.950638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T19:59:50.015277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-09-09T20:00:21.516508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-09T20:00:23.162898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-09T20:00:24.803700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-09T20:00:26.490508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-09T19:59:59.528020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-09T20:00:01.198695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-09T20:00:02.942364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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01000021Cash loansMNY0202500.0406597.524700.5351000.0UnaccompaniedWorkingSecondary / secondary specialSingle / not marriedHouse / apartment0.018801-9461-637-3648.0-2120NaN110110Laborers1.022WEDNESDAY10000000Business Entity Type 30.0830370.2629490.1393760.02470.03690.97220.61920.01430.000.06900.08330.12500.03690.02020.01900.00000.00000.02520.03830.97220.63410.01440.00000.06900.08330.12500.03770.0220.01980.00.00.02500.03690.97220.62430.01440.000.06900.08330.12500.03750.02050.01930.00000.00reg oper accountblock of flats0.0149Stone, brickNo2.02.02.02.0-1134.0010000000000000000000.00.00.00.00.01.0
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Last rows

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21000040Revolving loansMYY067500.0135000.06750.0135000.0UnaccompaniedWorkingSecondary / secondary specialSingle / not marriedHouse / apartment0.010032-19046-225-4260.0-253126.0111110Laborers1.022MONDAY9000000GovernmentNaN0.5559120.729567NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.0-815.0000000000000000000000.00.00.00.00.00.0
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